This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.

Every day we hear about the current state of the climate and how it is degrading very quickly. It is urgent to study ways in which people can change and improve their daily lives towards more sustainable habits. This paper studies graph-based simulation scenarios, focusing on both human and infrastructure characteristics, which could lead to a potential reduction in CO2 emissions. We compare alternative policies with an upper-bound base scenario, in which people use their own cars for their sole use. Most of these scenarios are based on Dijkstras shortest path algorithm, while others serve to underlie our reflections on the feasibility of using minimal spanning trees to solve the problem. The zone selected to illustrate this study is the coastal area of Espinho, a city in Portugal. It is shown that the scenarios with carpooling starting on the cut vertices of a tree generated by linking Dijkstras shortest paths of each agent yield the best results. This approach leads to a reduction in carbon emissions of between 25% and 30%.

eHUBS are on-street locations that bring together e-bikes, e-cargo bikes, e-scooters and/or e-cars, offering users a wide range of options to experiment and use in various situations. The idea is to give a high-quality and diverse offer of shared electric mobility services to dissuade citizens from owning private cars, resulting in a cleaner, more liveable and pleasant cities. The eHUBS project has worked together closely with the TOMP-API Working Group on a technical standard interface for communication between Transport Operators and MaaS Providers while keeping the needs of the public authorities and cities in close consideration. The TOMP-API allows all participating companies to communicate about planning, booking, execution, support, general information and payments of multimodal, end-user specific trips. This TOMP-API environment ensures growth in opportunities for interoperability between multiple parties. The presentation is going to show the importance of this standardisation and the power of the TOMP-API in optimizing data flows between Transport Operators and Maas Providers. Within the eHUBS project, we have created a kiosk application that presents travellers with a clear and understandable overview of (realtime) travel options & information within the direct vicinity of the eHUB, even when they don’t have access to a smartphone. This is achieved by collecting and interpreting data of the Transport Operators providing their services nearby the eHUB point, of course in close cooperation with the local authorities. Because of the local character, the data-sources and quality differ immensely. Some hubs are in densely connection urban areas, but they can also be situated in more rural surroundings. This application is what will be used to illustrate the strength of the TOMP-API, it will show how easy and fast it can be to implement new transport options and make it available directly. Especially when compared to the tedious process of combing to endless API descriptions and data-formats trying to find some unity and ways to reliably incorporate this. Supporting local Transport Operators in adopting this standard will be an important step forward for the digitalisation of the infrastructure in an effective and sustainable manner. The presentation will be hands-on and despite its technical nature, accessible and understandable for a broad audience.

Accurately modeling the realistic and unstable traffic dynamics of human-driven traffic flow is crucial to being able to to understand how traffic dynamics evolve, and how new agents such as autonomous vehicles might influence traffic flow stability. This work is motivated by a recent dataset that allows us to calibrate accurate models, specifically in conditions when traffic waves arise. Three microscopic car-following models are calibrated using a microscopic vehicle trajectory dataset that is collected with the intent of capturing oscillatory driving conditions. For each model, five traffic flow metrics are constructed to compare the flow-level characteristics of the simulated traffic with experimental data. The results show that the optimal velocity-follow the leader (OV-FTL) model and the optimal velocity relative velocity model (OVRV) model are both able to reproduce the traffic flow oscillations, while the intelligent driver model (IDM) model requires substantially more noise in each driver's speed profile to exhibit the same waves.

Autonomous vehicles (AVs) occupy a crucial part of the emerging mobility. Currently, the potential impacts of self-driving fleets are of great interest to public bodies and authorities. The impact is studied in pilots, but these still have a limited scope, therefore also simulation studies are necessary. This study aims to offer a comprehensive review of recent macro/mesoscopic agent-based models for AVs. Through keyword search, we extracted twenty-nine papers from the Web of Science database. These studies analysed AVs impact on the land-use, travel behaviour, environment and cost. Therefore, we summarise the similarities and particularities of modelling specifications and outcomes, meanwhile analysing the reasons for conflicting results. In general, the results from analysed papers are distinct from each other, and some even hold opposite results. There are several effective methods for increasing AVs customer acceptance. When implemented, it will bring significant amounts of benefit, including much lower energy consumptions, emission, operational costs and parking space demands. However, a more congested network and urban sprawl are the most unignorable problems. Finally, this research offers several suggestions and research directions regarding AV future development, which will be referential for both researchers and urban planners.

AbstractThis paper proposes a multilane traffic flow model based on the notions of conservation laws in Lagrangian coordinates. Both the mathematical formulation and the graphical representation are provided. A logit choice model is applied to describe drivers lane choice probability. The lane changing rate is estimated by employing the Incremental Transfer (IT) principle and the lane choice probability model. The numerical implementation of the model in the case of two lanes is discussed. The simulation results reveal that the proposed Lagrangian model is able to describe lane changing dynamics of vehicle platoons; while the lane changing equilibrium curve at the macroscopic level is consistent with that from the multilane Eulerian model as well as the observed data on the highway.

Many railway companies are experimenting with Automatic Train Operation (ATO) and most of them are aiming at grade of automation (GoA) level 2. This is the automation level whereas the train is driven automatically, and the driver is still fully responsible and also performing other tasks. The field of Human Factors studies the interaction between human beings and technology in order to optimize the performance delivered by this team. Human factors are a significant aspect in designing and improving the ATO implementation. Netherlands Railways (NS) has started experiments with ATO GoA2 in 2019. One of the main questions was how drivers felt in their cooperation with ATO. Our aim was to discover the (possible) interactions between ATO and drivers, to be able to evaluate and improve their cooperation and resulting performance. We know that people tend to forget important details when time evolves (e.g. Ebbinghaus forgetting curve). Our methodological question is how we could log the details of (possible) interactions precisely. Furtheron, we also want to create an anchor point such that drivers can remember the interaction again and is able to elaborate on the details. We came across the customer experience journey and noticed the similarities with the questions we want to be answered. Customer experience involves a constant feedback loop repeated throughout the usage lifecycle including from initial discovery through purchase, out-of-box, usage, maintenance, upgrades, and disposal (Beauregard et al. 2007). The customer experience consists of perceptions that shape emotions, thoughts, and attitudes. The customer journey maps direct and indirect touchpoints between customer and process, and indicates per touchpoint the emotions associated (Schmitt 1999). The customer journey comes from service design.

Since the early 19th century, we trust our lives in cars driving at high speeds in complex traffic. Trust in vehicle technology is justified since the vehicle design and production process has resulted in very high-reliability levels. Vehicle automation technology has not yet reached a level of maturity that is comparable to vehicle functioning in manual driving. A range of today’s passenger cars provides SAE Level 2 automation through Adaptive Cruise Control (ACC) and Lane Centring Systems, requiring the permanent supervision of human drivers to ensure the reliable and safe operation of the automated driving system (SAE, 2018). Drivers shall be aware of the limitations of Level 2 automation and shall not overtrust automation as this will lead to misuse and will threaten road safety. Higher automation levels (SAE Level 3+) gradually reduce the level of supervision of human drivers, allowing human drivers to direct attention away from the driving task and the supervision of the performance of the automated driving system to the engagement in eyes-off road activities. The efficient, comfortable and safe use of these higher automation systems requires high levels of trust in and perceived safety of vehicle automation technology. Hence undertrust can become a bottleneck in acceptance of Level 3+ automation. While automation levels and their operational design domain are gradually evolving in passenger cars and trucks, a more revolutionary introduction of automation is prepared in public transport. Driverless shuttles are being tested at low speeds in semi-public controlled environments under the supervision of safety stewards on board, but will we trust our lives to automated vehicles without steer and pedals and physical human supervision? In a recent survey where shuttle users were not aware of the presence of a safety steward, a majority of users indicated they would not feel safe without any type of human supervision by means of a remote control room or steward on board (Nordhoff et al., 2019).

Autonomous vehicles present opportunities for highly integrated multi-modal urban mobility services. This study reports on the evaluation of autonomous services that can be integrated with the existing public transport network. The autonomous services considered are first/last mile feeder services, on-demand point-to-point services, robo-taxis, and autonomous car-sharing and Bus Rapid Transit. In this evaluation, the views of users, public transport operators, public transport authorities and mobility service providers are taken into account. An in-depth understanding of the objectives for each stakeholder group is needed in order to assess the impact of new mobility services. Representatives from each stakeholder group were consulted to evaluate autonomous vehicle scenarios. Using the multi-actor multi-criteria analysis method, stakeholder criteria were weighted and used to calculate overall performance scores per scenario. The results indicate that users are positive towards all autonomous scenarios. For public transport operators all scenarios, except car-sharing, perform well. Public transport authorities believe more strongly in the benefits of on-demand point-to-point services, first/last mile feeders and bus rapid transport. Mobility service providers value flexible services most. These insights can be applied to evaluate the business models of public transport operators and mobility service providers and used to shape urban transport policies.

Acceptance is often claimed to be the crucial factor for autonomous vehicle success. In this study, we examine the factors that influence the acceptance of autonomous transit feeders using a hybrid choice model. Our research is based on data from a Germany-wide online survey with more than 1,000 respondents. The influence of gender, travel behavior, individual availability of mobility tools, such as car ownership or transit pass, and previous knowledge coincides with findings from previous studies. Further, we are able to explain a large part of the unexplained heterogeneity compared to a base ordered probit model with the latent variables simplification through autonomous minibuses and pro-transit-attitude. This indicates the relevance of considering attitudes in future research on the acceptance of autonomous vehicles in order to arrive at correct interpretation and to reduce the heterogeneity in predicting models.

Electrifying mobility-on-demand (MoD) fleets is an important step towards a more sustainable transportation system. With increasing fleet size, MoD operators will be able to participate in the energy exchange market and will have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of i.e. a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a computationally very efficient model to study the trade-offs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet sizes and different numbers of charging units, we compare the performance of several reactive and scheduling policies. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.

Electric freight vehicles (EVs) are a sustainable alternative to conventional internal combustion freight vehicles. The driving autonomy of EVs is a fundamental component in the planning of EV routes for goods distribution. In this respect, a complicating factor lies in the fact that EVs' energy consumption is subject to a great deal of uncertainty, which is due to a number of endogenous and exogenous factors. Ignoring such uncertainties in the planning of EV routes may lead a vehicle to run out of energy, which -given the scarcity of recharging stations- may have dire effects. Thus, to foster a widespread use of EVs, we need to adopt new routing strategies that explicitly account for energy consumption uncertainty. In this paper, we propose a new two-stage stochastic programming formulation for the single electric vehicle routing problem with stochastic energy consumption. Furthermore, we develop a decomposition algorithm for this problem. We provide an illustrative example showing the added value of incorporating uncertainty in the route planning process. We perform a variety of computational experiments and show that our decomposition algorithm is capable of efficiently solving instances with 20 customers and 30 scenarios.

Public transport systems, being a safe and sustainable mode of transport, can benefit to a great extent from availing Autonomous vehicle (AV)s. However, the advancements in the road transport infrastructure is not at par with advancements in Intelligent Transportation System (ITS) and vehicle technologies. Cities must consider these changes to realize AVs as a public transport mode. In this paper, we propose three bus-bay designs to integrate AVs with public transport to address this issue. We develop a microscopic traffic simulation model to find the effectiveness of these designs with traffic data obtained from local transport authority and field measurements. The designs are simulated in Singapore city and could be adopted to other cities. Results show that an exclusive AV bay which is physically separated from bus bay reduced the travel time of AV by 5%. Queue length, at the signal near bus stop, has decreased by 27% for such a design.

Recent research has demonstrated the potential benefits of connected, autonomous vehicles (CAVs) to the performance of urban networks. Specifically, several proposals have been made for policies and related technologies that either perform more efficiently when the proportion of CAVs is relatively high or that exclude human driven vehicles (HDVs) altogether. This same body of research has also identified several challenges faced by such networks, especially in the context of shared autonomous vehicles (SAVs). We propose a lane-use policy for networks of exclusively CAVs with the goal of preserving priority within any two-class, arbitrary priority assignment regime. We investigate the merits of such a policy by adopting a simple occupancy-based, two-class priority scheme in a network of SAVs. We will demonstrate that by granting and preserving priority for occupied vehicles, average travel times and speeds for passengers are improved with limited degradation in these measures for other, i.e. unoccupied, vehicles. The proposed lane-use policy is developed on realistic physical limitations of the street network and without the need for trajectory reservations.

Autonomous vehicles are believed to change the way we will perceive travel time as the car would no longer be driven by a person, enabling other activities to be performed during the time normally used for controlling the vehicle. Especially for private car commuting, scholars suggest that the value of travel time would decrease substantially due to the possibility to work during the car ride. Travel distance might increase in return, as time used during commuting will not be perceived as time lost. This could lead to the preference of living in rural areas, where land prices and rents are typically lower, and, ultimately, to urban sprawl. By looking at the jobs and mobility characteristics of the Swiss population, we argue that only a small percentage of the total population would actually benefit from active use of travel time during commuting, taking away the fear of urban sprawl through automated vehicles.

Cities are estimated to have a 70% contribution to global greenhouse gas emissions. This makes urban sustainability monitoring necessary, but are urban sustainability monitoring frameworks applicable to cities of all sizes? And do they offer a consistent overview of the sustainability status of core urban development areas, such as transport? The present research tests if the specific needs of small and medium-sized Norwegian cities, as perceived by local stakeholders, are consistently covered by the indicators of urban sustainability monitoring frameworks. To this purpose, four international frameworks were evaluated in the frame of a workshop: Reference Framework for Sustainable Cities, Key Performance Indicators for Smart Sustainable Cities, ISO 37120:2018 Sustainable cities and communities Indicators for city services and quality of life, and LEED for Cities (pilot phase). The evaluation was done by local and regional representatives of academia, the private sector, and public authorities with expertise in urban planning. A set of dedicated transport indicators was also evaluated. The results highlight the alignment between urban and transport sustainability indicators and the perceived needs of Norwegian small and medium-sized cities. These results pave the road for urban sustainability monitoring frameworks to better shape their tools towards the needs of small and medium-sized cities.

The mobility system is undergoing a paradigm shift from fossil fuel-based mobility towards carbon neutrality and greater energy efficiency. Yet this transformation is still in its infancy. In order to reach the CO2 target defined by the Paris Agreement, an increased use of sharing and electric vehicles is suggested. While many scholars have already investigated the factors relevant for promoting the use of sharing or electric vehicles, less is known about the interplay between experience with carsharing and future car buying decisions. We thus adopted a stated choice survey with 995 participants randomly drawn from the German and French-speaking population of Switzerland to test the drivetrain purchase preferences of users with and without carsharing experience. Results suggest that carsharing users are two times more likely to buy an electric-drive vehicle, i.e. battery electric, plug-in hybrid or hybrid electric vehicle, compared to non-carsharing users, even after controlling for socio-demographics, mobility characteristics, values and pro-environmental attitudes.

The main purpose of this work is to develop a single vehicle noise emission model that uses speed as input variable and returns as output a parameter directly referable to the noise source, namely the source sound power level (Lw). The model was tested on three light-duty vehicles with different motorizations: diesel, gasoline and gasoline-electric hybrid. Field measurements were conducted on a straight road and for different speed values (10-90 km/h). The influence of the engaged gear on the noise at different constant speed values was also explored for gasoline and diesel vehicles using one-way analysis of variance (ANOVA). Results revealed that the source power level emitted by different typologies of cars against speed followed significantly different trends, more evident at speeds lower than 40 km/h. In such cases, the contribution of the engine on the noise is prevalent and ANOVA test confirmed that the gear choice influenced the noise at low speeds. At higher speed values such difference disappears.

A new design of a user-centric Emission Trading Systems (ETS) and its implementation as a carbon Blockchain framework for Smart Mobility Data-market (cBSMD) is presented. The cBSMD allows for individual transactions of token-based GHG emission quantities when realizing a trip in a multimodal setting as well as the management of system-wide emission performance data. The cBSMD design is here applied to an ETS framework where individual travellers receive a certain amount of emission credits in the form of tokens. Travellers spend tokens every time they emit GHG when travelling in a multi-modal network through cBSMD transactions. This design instance of cBSMD is then applied to a case-study of 24hours of mobility for 3,187 travelers. The cBSMD performs with a very low latency and high throughput for this number of travelers. To showcase cBSMD data management features, socio-demographic and trip features regarding token usage and emission performance are also analyzed. Our proposed system sets the first implementation step towards the design of future user-centric and practice-ready ETS frameworks.

The application of automation in vehicles has increased rapidly over the years, especially with regard to automated driver support systems. Vehicle automation is proclaimed as a remedy for improved road safety, mobility and cleaner environment. The claim for cleaner environment is based on more efficient driving, so-called “tank to wheel”. What is ignored in this claim is the increase of data required to allow automation. If this data requirement is included and translated into required additional energy production, vehicle automation may jeopardize the environment by causing considerable amounts of additional CO2 and pollutant particle emissions. This article will provide details in the calculation and the magnitude of these effects. Keywords: Automation, data, emission

The era of intelligence transportation is coming. Nonetheless, the transition to an intelligent system will be a gradual process. On the one hand, some zones in the city may be dedicated as autonomous zone with a fully intelligent traffic facility allowing only autonomous vehicles. On the other hand, autonomous and conventional vehicles are both allowed to drive in the remains zone of the network. In this paper, we consider a situation where AVs are deployed by a taxi operating company to serve door-to-door travel requests. Facing this transition period, a flow-based vehicle routing model is developed to determine the optimal fleet size of autonomous and conventional taxis as a function of the gradually increasing coverage of the automated vehicles- only area. Traffic congestion is considered as a dynamically varying travel time with the vehicle flows. In this paper, two service regimes of the company are tested: User Preference Mode (UPM) and System Profit Mode (SPM). The developed model formulations are applied to the case study city of Delft, the Netherlands. The results give insight into the performance of the heterogeneous taxi system on a hybrid network. Strategies are presented on how to adjust the fleet size of autonomous and conventional taxis to get the best system profit while satisfying the mobility demand. The SPM can bring more profit to the operating company by reducing the detour and relocation distance of taxis compared to the UPM.

In the past decade, shared mobility systems involving station-based, dockless and electric bikesharing, and electric scooter sharing (collectively branded as ‘micromobility’ services) have emerged as increasingly popular modes for short trips in urban transportation landscape worldwide. A robust revenue stream is a necessary element of economic sustainability of these services. Bikeshare fare products and pricing plans for casual users (temporary users with no long-term commitment) and members (or subscribers) vary from system to system and change over time. Known as 4Ps of the marketing mix in the marketing parlance, four basic elements of marketing plan, namely: product, price, place and promotion, help develop marketing strategies and tactics (McCarthy, Shapiro & Perrealt 1979). In this research, we conduct a closer scrutiny of reactions of casual users of bikeshare to products (or services) and their pricing. Value-based pricing, which considers customer perspective, increases the likelihood of maximizing revenues from the same set of customers simply by altering their product-selection from the given product mix (Hinterhuber 2008). We define value-based pricing as a strategy for setting the price of a product or service that offers economic value to consumers. The value may be absolute or relative to other products in the choice set and it may be real or perceived. We hypothesize that by introducing value-based pricing options into the fare product mix, micromobility service revenues can be increased thereby enhancing the economic sustainability of the system. We test the hypothesis by conducting a controlled experimental survey of 157 current and potential casual users of bikeshare across six cities in the United States. Respondents’ choices of fare products were registered in two groups – the binary choice set (CS-1) as a control group and the other choice set (CS-2) with an additional value-priced fare as an experimental group.

As evidenced by their rapid adoption in recent years, shared micro-mobility services have resonated with consumers as first and last-mile solutions by penetrating the traditional transportation framework. A recent upsurge in investments from tech industry competitors like Uber, Lyft, Ford, Alphabet, and other venture capital firms points to the likelihood of even more rapid growth in shared micromobility services in the future. Unlike the station-based bike-sharing, which gained steady traction by evolving over a dozen years, e-scooter as a micromobility mode has a recent phenomenon. Often cities are making instant decisions without the benefit of mode choice behaviour. The advent of e-scooters led to the near disappearance of dockless bikesharing system and rapid decline in trips using dock-based bikesharing. Regardless of their mixed reception from the public due to numerous safety concerns, e-scooters outperformed other micromobility services doubling the overall micromobility ridership to 84 million trips in just one year [1]. Very little is known about the adaptable behavior and mode-choice preferences of these growing micromobility users that are critical for policymaking, planning and operations. Several researchers have surveyed users, quantified demand-supply dynamics, modeled user behavior, developed predictive models, and documented noteworthy findings of station-based bikesharing services in the past few years [2]–[5]. These studies addressed user demographics and their mode-choice preferences and the spatial equity of service.

Real-time systems require up-to-date information. Measurement signals in the power train of Electric Vehicles (EVs) are however often received with individual time delays due to the distributed architecture of the power train. Our idea is to compensate the time delays by predicting each signal from the last received value until the present time step. In this work, we evaluate 5 state-of-the-art algorithms and 2 naive methods for time series prediction. We execute all algorithms on real power train data of EVs and compare the results. Our evaluation focuses on run-time and accuracy. All methods achieve a prediction error rate of less than 5 %. As expected, the benchmark naive method is the fastest. Surprisingly, it retrieves comparable results to Exponential Smoothing. BATS and TBATS are the slowest methods. Nevertheless, they achieve the best accuracy, but suffer from outliers. Auto-Regressive Integrated Moving Average (ARIMA) achieves the smallest Mean Absolute Percentage Error (MAPE) and thus the best compromise between outliers and accuracy of all algorithms. Additionally, to further improve the accuracy, we investigate Additionally, to further improve the accuracy, we investigate the benefits of combining predictions of different algorithms.

The design of hybrid electric vehicles based on real-world driving conditions becomes increasingly important due to strict legislation and complexity of future powertrain concepts. Therefore, component sizes should be optimized not only with respect to fuel consumption but also regarding real-world load conditions. Since e.g. a proper design of the cooling system of electrical components requires consideration of time-related power demand, this paper proposes a novel concept of incorporating time frame-based load analysis (TFBA) into driving cycle synthesis. The synthesis is carried out within a two-layer optimization framework where the first layer considers statistical features and the second considers quantities directly related to a vehicles' load implication. By taking into account Markov chain theory, a class frame is derived which then serves as design space for optimizing a sequence of micro-trips with respect to meeting target criteria based on the concepts of mean tractive force and TFBA. To prove practicality, an exemplary driving cycle is synthesized and the method is validated within a parameter study. Results show that the synthetic driving cycle is representative with respect to the considered target criteria and moreover statistical quantities used for validation are within an error tolerance of 5%.

In present study we focus on a special case problem for an island where the transportation is mainly based on barouches pulled by horses due to the limitation on the use of internal combustion engine vehicles. Both the daily travels of islanders and touristic tours of visitors are provided by barouches that are operated as private travel mode, where they serve to meet individual travelers demands. Our motivation has therefore been to alternate barouches with electric vehicles in conjunction with determining the locations of recharging stations in order to respect the animal rights and pollution concerns. Considering the rugged topology of the case area, we notice the limitations and benefits of EVs. On one hand, noticing the slope climbing restrictions and limited driving range of EVs may be accepted as the most significant challenges of alternating the vehicle types of the fleet. On the other hand, the potential of regenerative energy, together with the reduced emission effects, is a promising feature of EVs. In this context, we formulate an Electrified Location Routing Problem using mixed integer linear programming by realistically calculating the battery load considering explicitly the driving resistances and the potential regenerative braking.

This paper proposes a model for charge scheduling of electric vehicles in last-mile distribution that takes into account battery degradation. A mixed integer linear programming formulation is proposed that minimizes labor, battery degradation and time-dependent energy costs. The benefit of implementing charge schedule optimization is assessed for a real-life case study at e-grocer Picnic. It is shown that charging optimization yields an overall reduction of charging costs by 25.2% when compared to the current operational charging performance. Furthermore, the impacts of three different shift schedule types, the increase in vehicle battery size and the coordinated charging are investigated. It turns out that more energy demanding shift schedules result in higher average charging cost per charged amount of energy. The introduction of a larger battery size as well as coordinated charging show potential for decreasing overall costs.

The dynamic origin-destination (OD) information is crucial for traffic operations and control. This paper presents a dynamic traffic demand prediction framework based on an anomaly detection algorithm. The Principal Component Analysis (PCA) method is applied to extract main demand patterns which are used to detect the abnormal conditions. The proposed approach can select prediction methods (parametric or nonparametric) automatically based on the pattern detection results. Both simulation and field observed Automatic Number Plate Recognition (ANPR) data are used to verify the proposed approach where the Kalman filter model and the K-nearest neighbor model are chosen as the basic prediction methods. The results show that the prediction framework can effectively reduce the noise of a single prediction model particularly in the abnormal conditions and provide more accurate and reliable prediction results.

AbstractThe Yangtze River is the first of China and the third-longest river in the world. It is the most developed inland water transportation system in China. Chongqing, located in the upstream of the Three Gorges Project, is not only the gateway of the industrial and commercial center of China but also the most abundant water and intermodal inland hub and material distribution center in the southwest. Frequent water traffic accidents pose severe threats to the safety of human life and property and the water environment as well as bring adverse effects on social stability. Therefore, based on the ten years of statistical data of ship accidents in 2009-2018 from the Chongqing Maritime Safety Administration (MSA), this paper summarizes the characteristics of the spatiotemporal distribution of accidents through statistical analysis of the historical data. Moreover, it proposes accident prevention and supervision methods to provide decision support for maritime safety. This research is of great significance to the prevention and control of water traffic accidents in this region. Index TermsShip accidents, statistical analysis, water transportation, risk analysis, data visualization, maritime safety.

In this paper we propose a novel observer-based method for anomaly detection in connected and automated vehicles (CAVs). The proposed method utilizes an augmented extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model with time delay, where the leading vehicle's trajectory is used by the subject vehicle to detect sensor anomalies. We use the classic $chi^2$ fault detector in conjunction with the proposed AEKF for anomaly detection. To make the proposed model more suitable for real-world applications, we consider a stochastic communication time delay in the car-following model. Our experiments conducted on real-world connected vehicle data indicate that the AEKF with $chi^2$-detector can achieve a high anomaly detection performance.

1. Problem statement and contribution E-commerce is a rapidly growing and evolving sector. The sector is however struggling with its organization of the last-mile deliveries in order that it meets the sustainability requirements, both economically and environmentally. Different delivery methods translate into different environmental impacts. Several studies have established the advantages of collection points in terms of air pollution and/or CO2 emissions in comparison to home delivery (McLeod et al., 2006; McLeod and Cherrett, 2009; Song et al., 2009; Edwards et al., 2010; Giuffrida et al., 2016; Carotenuto et al., 2018). Zhang et al. (2018) and Verlinde et al. (2019) reach similar conclusions but only given a specific time of consumer behavior, identifying consumers’ collection trips as determining factor in comparing the environmental impact. Yet, these studies all apply for urban areas. However, consumers of all places (also suburban and rural) engage in online retailing. Moreover, web-shops hardly focus on urban inhabitants solely and often take a national approach. Given that the results of available research is bound to the local/urban context, their conclusions cannot be generalized or transferred to rural or suburban areas. 2. Methodology Following our objective to address this gap, we investigated the environmental impact associated with four last mile delivery methods from the perspective of non-food retail products in Belgium. We compared deliveries originating from a dedicated distribution center to homes (direct with 3PL or via dedicated local distribution centers) and collection points (direct or via store supply) in terms transport-related external costs for CO2 emissions, air polluting emissions, accidents, noise nuisance, infrastructure and congestion and assess the differences in impact between consumers’ residences, specifically urban, urbanized and rural areas. Both logistics flows and customer movements to the collection points were considered. To do so we applied the agent-based simulation model TRABAM (Mommens, 2019). The model uses MATSim and extends the Freight Extension (Schröder et al., 2012). 3. Results The results indicate on the one hand that homedeliveries via a well-established 3PL (with high daily volume i.e. 250.000 parcels) is the most sustainable scenario for urban, urbanized and rural areas. Deliveries to collection points are in none of the considered scenarios more sustainable than homedeliveries via 3PL, this is due to the customer movements. Yet it has to be noted that the difference between homedeliveries via 3PL and deliveries to collection points via store supply is very small to almost neglectable for the urban area. The difference becomes bigger for urbanized area and is the biggest for the rural area. Organizing a proper homedelivery system is not a sustainable option for the considered case. On the other hand, significant differences (between 10 and 15%) in terms of sustainability were found within scenario’s between urban, urbanized and rural areas. Both indicate that the environment (urban, urbanized, rural) should be considered as a parameter.

In this paper we present a methodology to more accurately calculate emissions in regional freight transportation models by taking the loading rate of vehicles into account in the emission factors. This is done by incorporating the shipment-based demand data from a multi-agent simulation framework into the route assignment. In a case study we show that disregarding the vehicle loading rate can lead to underestimations of the amount of pollutant gasses. The application of this methodology in combination with a multi-agent simulation model allows to simulate impacts of changes in logistic behavior, such as vehicle type use or the scheduling of round tours, on emissions more accurately.

The paper presents a model to estimate the demand of parcels by disaggregating nation-wide commodity flows. Secondly, the model generates parcel delivery tours to quantify transport-related effects. This model is applied to simulate parcel deliveries using cargo bikes. It consists of a two-step process: first, micro depots located close to the demand are fed with vans; second, parcels from micro depots to the customers are distributed by cargo bikes. The model simulates different shares of cargo bikes vs. motorized vans to deliver the same demand. We also studied the effect of micro depot densities and different parcel demand intensities in the same catchment area. Lastly, we compared the cargo bike tours at locations with different demand densities (parcels/km2). The results find beneficial effects of cargo bikes when the demand density and the share of cargo bikes is high. Under these conditions, the total vehicle-kilometer traveled and motorized vehicle emissions can be reduced.

A successful implementation of autonomous vehicles will depend to a great extent on the acceptance of users. The individual attitudes regarding AVs are crucial in the adoption of this technology. Numerous autonomous shuttle projects have been deployed worldwide to gather insights on the technical behavior of these shuttles. Certain projects have a specific focus on user experience and acceptance of autonomous technology. As mobility patterns vary across geographic regions, the current paper investigates the user acceptance of an autonomous shuttle in the Brussels capital region with 220 respondents, 145 of which are shuttle passengers, 75 are other road users. The survey includes questions and recommendations from previous experience studies as well as relevant items from the Unified Theory of Acceptance and Use of Technology (UTAUT2). Aside from the vehicle passengers, also other road users in the proximity of the shuttle were invited to fill out a survey. The results from the study provide interesting insights for both passengers and other road users. Both groups are overall optimistic and positive towards AVs. Furthermore, adding an autonomous shuttle to traffic does not significantly influence the feeling of safety. Lastly, men are found to be more positive towards AVs than women.

Shared e-mobility is a category of emerging mobility services that includes electric carsharing, e-bike sharing and e-scooter sharing. These services are expected to reduce the negative externalities of road transport that is currently dominated by fossil-fuel-powered private car trips. In order to better inform the development and promotion of these services and indicate directions for further research, we conduct a comprehensive review of existing literature on the three shared e-mobility modes focusing on their usage pattern, demand estimation and potential impacts. We found that despite the different vehicle capabilities, all three shared e-mobility services are mainly used for short trips, and their current users are mostly male, middle-aged people with relatively high income and education. The demands of all shared e-mobility modes share many common predictors: they appeal to people with similar socio-demographic characteristics and generate higher demand in locations with better transport connection and more point of interests. Shared e-mobility services can potentially lead to positive impacts on transportation and the environment, such as reducing car use, car ownership, and greenhouse gas emissions. However, the magnitude of these benefits depends on the specific operating conditions of the services such as the fuel type and lifetime of shared vehicles. The impact of each shared e-mobility mode is also expected to be affected by other coexisting shared e-mobility modes due to both complementarity and competition. Future directions should include studying the competition between and integration of multiple shared e-mobility modes and the effect of automation.

Automated driving will trigger disruptive changes in the transportation system. Automated sharing and pooling options instead of private ownership are broadly discussed because of their possible contributions to climate change mitigation and sustainability. Despite the growing amount of literature on the adoption of these alternatives, little empirical evidence is available on the potential drivers of adoption, such as individuals socioeconomic background, mobility characteristics, attitudes, and values. To address this gap, we utilize the results of an online choice experiment involving 709 participants from Switzerland, which tested future mode choices considering automated cars, automated pooled-use taxis, and automated public transport shuttles, both for short- and long-term mobility decisions. Exploratory regression analysis explains the experiment outcome with a broad set of underlying data predicting willingness to use. Our findings illustrate that automated cars and automated public transport often appeal to the user groups of their traditional non-automated counterparts. However, this does not seem to be the case for automated pooled-use taxis, which we find to be associated with higher-income groups. Attributes on current mobility characteristics and values cannot be significantly associated with automated pooled-use taxis. We also demonstrate that short- and long-term mobility decisions are worth studying together in AV adoption studies.

In this work, we develop different scenarios regarding the initiatives and incentives to foster the use of electric vehicles (EVs) and test the different results on an optimal allocation tool for charging stations (ETCharger [1]) in The Hague city, the Netherlands. This is a sub-task from project ELECTRIC TRAVELLING [2]. Firstly, we overview the initiatives and incentives that have been implemented to foster the use of EVs in different regions in Europe [3] and analyzes the incentives that will be used in the implementation of the project to foster electromobility (e-mobility) in smart cities. Then the database of the city The Hague including existing transport network, and transport infrastructure parameters, especially the ones related to e-mobility etc., are introduced in order to apply the proposed tool to the case study. Based on the analysis focused on the incentives, we develop different scenarios of the transport system and use them to allocate charging stations. A comparison is done between different scenarios to select the optimal station locations according to the main goal of the project: foster increase the number of travellers by using EVs.

Air pollution and in particular PM2.5 emissions are a major problem worldwide. Road transport is a significant contributor to PM2.5 emissions in urban areas and as such it is important to understand and be able to accurately model the effects of vehicles on PM2.5 emissions. In this paper a computer vision algorithm is introduced which is able to extract vehicle trajectories from video footage. The algorithm has a 100% accuracy for overall total vehicle counting. Comparing the speeds predicted by the computer vision script to manually following a single vehicle feature on the video file, the average relative speed accuracy is 2.7% at a 1 Hz time resolution. Using these vehicle trajectories in an instantaneous vehicle emissions model and also as input to COPERT v5, tailpipe PM2.5 emissions were estimated and compared to on-road measurements. It was shown that a local sensor is not sufficient to determine vehicle tailpipe emissions due to the influence of meteorological conditions and other emission sources. Combining computer vision with an instantaneous vehicle emissions model is a useful method to evaluate changes in emissions caused by transport policies.

Many alternative approaches to achieve improvement in transportation system sustainability are continuously being proposed. The sheer number and diversity of suggested approaches available in the literature is overwhelming. Decision makers, consequently, need support in the fair and comprehensive evaluation, comparison, and combination of these proposed initiatives. A methodology for the generation of scientific-based decision support on the development of truly integrated strategies to improve transport sustainability is presented in this paper. The key contribution of the approach produced is that all the underlying complexities in the formulation of such strategies are adequately addressed and incorporated in its design. This is achieved through the development of a customized multi-objective metaheuristic simulation optimization model, called the Transport Energy Management Tool (TEMT). A case study application of the TEMT is used to demonstrate its success and usefulness and serves as a proof of concept for adoption of this approach in sustainable transport management.

Although the electrification of transportation can bring long-term sustainability, increasing penetration of Electric Vehicles (EVs) may cause more congestion. Inappropriate deployment of charging stations not only hinders the EVs adoption but also increases the total system costs. This paper attempts to identify the optimal locations for fast-charging stations in the urban network considering heterogeneous vehicles with respect to the traffic congestion at different levels of EVs' penetration. A bi-level optimization framework is proposed to solve this problem in which the upper level aims to locate charging stations by minimizing the total travel time and the infrastructure costs. On the other hand, the lower level captures re-routing behaviors of travelers with their driving ranges. Finally, numerical study is performed to demonstrate the fast convergence of the proposed framework.

Every year, some 4.5 million containers are being discharged in ports around the Netherlands. Most containers arrive on seagoing vessels in the Ports of Rotterdam and Amsterdam, from where they continue their journey on inland vessels, trains and heavy goods vehicles (HGVs) towards their destination, either within the Netherlands or elsewhere in Europe. To date, there is no complete overview of all this container transport. Statistics Netherlands (CBS) is studying possible ways of mapping the container routes within the country from beginning to end in a pilot study commissioned by Rijkswaterstaat (Department of Waterways and Public Works in the Netherlands, tr.) Essential elements in the process are new data from transport companies and customs data. Incomplete data CBS keeps track of both goods flows and transport flows through the Netherlands. The statistics on freight transport are broken down by mode: maritime and inland navigation, railway and road transport. For certain types of goods such as bulk commodities – e.g. coal and ore – this yields a fairly accurate picture. Bulk commodities tend to be carried by one single mode of transport (unimodal). However, the breakdown is less suited for other types of cargo such as containers: ‘Containers are often transhipped, moving from one mode to another,’ explains Mathijs Jacobs, traffic and transport researcher at CBS. ‘When you look at container transport broken down by mode, the data on transhipment movements is limited and there is a risk of double counting goods in the overall picture. Source data for the freight transport statistics include registers and surveys based on sampling, but these are incomplete. We fill the information gaps with estimation models. These are based on assumptions and they don’t cover the entire chain of freight transport. Especially data on individual container loads and the routes they travel are lacking.’

Analyzing and visualizing traffic data in order to better understand congestion trends, safety concerns, goods movement and capacity needs is a pressing need. Broadly speaking, there is a large amount of traffic data available today, including volume, lane occupancy, speed, and travel time, which can be used to manage transportation networks, provide traveler information and produce performance measures. This broadly disseminated data almost always treats all vehicles alike, without discriminating between trucks and passenger cars. Since trucks are critical and growing components of freeway traffic, monitoring and tracking their dynamics can reveal the impacts of freight movement on current freeway operations and over time will uncover trends useful for future planning and management. This study takes advantage of a unique data stream available for the freeway network of Portland, Oregon, USA. In addition to providing continuous vehicle count, speed and lane occupancies at 20-second intervals at more than 500 stations (1,300 individual detectors), the Portland system reports volume bins at 4 length-based classifications (< 20, 20-35, 36-30, >60 ft). Given that most vehicle classification studies are done over very short time intervals at an extremely limited number of locations, this nonstop data stream enables unprecedented insight into where and when trucks are traveling on Portlands freeways and a wealth of opportunities for performance measurement and diagnosis of their impacts. The objective of this paper is to exploit this new data stream and explore new visualization techniques that depict truck volume, truck percentage and volume-weighted average vehicle speeds along Portlands Interstate 5 corridor, an important north-south freight route between the Canada and

This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.

Every day we hear about the current state of the climate and how it is degrading very quickly. It is urgent to study ways in which people can change and improve their daily lives towards more sustainable habits. This paper studies graph-based simulation scenarios, focusing on both human and infrastructure characteristics, which could lead to a potential reduction in CO2 emissions. We compare alternative policies with an upper-bound base scenario, in which people use their own cars for their sole use. Most of these scenarios are based on Dijkstras shortest path algorithm, while others serve to underlie our reflections on the feasibility of using minimal spanning trees to solve the problem. The zone selected to illustrate this study is the coastal area of Espinho, a city in Portugal. It is shown that the scenarios with carpooling starting on the cut vertices of a tree generated by linking Dijkstras shortest paths of each agent yield the best results. This approach leads to a reduction in carbon emissions of between 25% and 30%.

eHUBS are on-street locations that bring together e-bikes, e-cargo bikes, e-scooters and/or e-cars, offering users a wide range of options to experiment and use in various situations. The idea is to give a high-quality and diverse offer of shared electric mobility services to dissuade citizens from owning private cars, resulting in a cleaner, more liveable and pleasant cities. The eHUBS project has worked together closely with the TOMP-API Working Group on a technical standard interface for communication between Transport Operators and MaaS Providers while keeping the needs of the public authorities and cities in close consideration. The TOMP-API allows all participating companies to communicate about planning, booking, execution, support, general information and payments of multimodal, end-user specific trips. This TOMP-API environment ensures growth in opportunities for interoperability between multiple parties. The presentation is going to show the importance of this standardisation and the power of the TOMP-API in optimizing data flows between Transport Operators and Maas Providers. Within the eHUBS project, we have created a kiosk application that presents travellers with a clear and understandable overview of (realtime) travel options & information within the direct vicinity of the eHUB, even when they don’t have access to a smartphone. This is achieved by collecting and interpreting data of the Transport Operators providing their services nearby the eHUB point, of course in close cooperation with the local authorities. Because of the local character, the data-sources and quality differ immensely. Some hubs are in densely connection urban areas, but they can also be situated in more rural surroundings. This application is what will be used to illustrate the strength of the TOMP-API, it will show how easy and fast it can be to implement new transport options and make it available directly. Especially when compared to the tedious process of combing to endless API descriptions and data-formats trying to find some unity and ways to reliably incorporate this. Supporting local Transport Operators in adopting this standard will be an important step forward for the digitalisation of the infrastructure in an effective and sustainable manner. The presentation will be hands-on and despite its technical nature, accessible and understandable for a broad audience.

This paper aims to optimize on-ramp merging processes for connected automated vehicles by utilizing an existing hierarchical control architecture including a decision-maker and an operational controller. The decision-maker employs surrogate linear models to predict future vehicular acceleration analytically and computes a merging sequence to minimize merging times of on-ramp vehicles. The operational controller is formulated as a model predictive control problem, which utilizes a second-order vehicle dynamics model, and regulates vehicles accelerations and time instants to execute lateral movements of on-ramp vehicles for the merging processes respectively. Constraints on vehicular acceleration, speed, and inter-vehicle distance are considered by the decision-maker and the operational controller for practical usage. The proposed method to minimize the merging times of on-ramp vehicles and a first-in-first-out method are tested under different initial settings, including initial vehicular speeds, distributions of vehicular positions, and desired time gaps. The simulation results show that the proposed method is superior to the first-in-first-out method widely used in literature in improving merging traffic efficiency. We find that cooperation among vehicles makes the on-ramp vehicles join mainline traffic faster, and the acceptable time gap for merging affect choices of optimal merging sequences.

Autonomous vehicles (AVs) occupy a crucial part of the emerging mobility. Currently, the potential impacts of self-driving fleets are of great interest to public bodies and authorities. The impact is studied in pilots, but these still have a limited scope, therefore also simulation studies are necessary. This study aims to offer a comprehensive review of recent macro/mesoscopic agent-based models for AVs. Through keyword search, we extracted twenty-nine papers from the Web of Science database. These studies analysed AVs impact on the land-use, travel behaviour, environment and cost. Therefore, we summarise the similarities and particularities of modelling specifications and outcomes, meanwhile analysing the reasons for conflicting results. In general, the results from analysed papers are distinct from each other, and some even hold opposite results. There are several effective methods for increasing AVs customer acceptance. When implemented, it will bring significant amounts of benefit, including much lower energy consumptions, emission, operational costs and parking space demands. However, a more congested network and urban sprawl are the most unignorable problems. Finally, this research offers several suggestions and research directions regarding AV future development, which will be referential for both researchers and urban planners.

AbstractThis paper proposes a multilane traffic flow model based on the notions of conservation laws in Lagrangian coordinates. Both the mathematical formulation and the graphical representation are provided. A logit choice model is applied to describe drivers lane choice probability. The lane changing rate is estimated by employing the Incremental Transfer (IT) principle and the lane choice probability model. The numerical implementation of the model in the case of two lanes is discussed. The simulation results reveal that the proposed Lagrangian model is able to describe lane changing dynamics of vehicle platoons; while the lane changing equilibrium curve at the macroscopic level is consistent with that from the multilane Eulerian model as well as the observed data on the highway.

Many railway companies are experimenting with Automatic Train Operation (ATO) and most of them are aiming at grade of automation (GoA) level 2. This is the automation level whereas the train is driven automatically, and the driver is still fully responsible and also performing other tasks. The field of Human Factors studies the interaction between human beings and technology in order to optimize the performance delivered by this team. Human factors are a significant aspect in designing and improving the ATO implementation. Netherlands Railways (NS) has started experiments with ATO GoA2 in 2019. One of the main questions was how drivers felt in their cooperation with ATO. Our aim was to discover the (possible) interactions between ATO and drivers, to be able to evaluate and improve their cooperation and resulting performance. We know that people tend to forget important details when time evolves (e.g. Ebbinghaus forgetting curve). Our methodological question is how we could log the details of (possible) interactions precisely. Furtheron, we also want to create an anchor point such that drivers can remember the interaction again and is able to elaborate on the details. We came across the customer experience journey and noticed the similarities with the questions we want to be answered. Customer experience involves a constant feedback loop repeated throughout the usage lifecycle including from initial discovery through purchase, out-of-box, usage, maintenance, upgrades, and disposal (Beauregard et al. 2007). The customer experience consists of perceptions that shape emotions, thoughts, and attitudes. The customer journey maps direct and indirect touchpoints between customer and process, and indicates per touchpoint the emotions associated (Schmitt 1999). The customer journey comes from service design.

Since the early 19th century, we trust our lives in cars driving at high speeds in complex traffic. Trust in vehicle technology is justified since the vehicle design and production process has resulted in very high-reliability levels. Vehicle automation technology has not yet reached a level of maturity that is comparable to vehicle functioning in manual driving. A range of today’s passenger cars provides SAE Level 2 automation through Adaptive Cruise Control (ACC) and Lane Centring Systems, requiring the permanent supervision of human drivers to ensure the reliable and safe operation of the automated driving system (SAE, 2018). Drivers shall be aware of the limitations of Level 2 automation and shall not overtrust automation as this will lead to misuse and will threaten road safety. Higher automation levels (SAE Level 3+) gradually reduce the level of supervision of human drivers, allowing human drivers to direct attention away from the driving task and the supervision of the performance of the automated driving system to the engagement in eyes-off road activities. The efficient, comfortable and safe use of these higher automation systems requires high levels of trust in and perceived safety of vehicle automation technology. Hence undertrust can become a bottleneck in acceptance of Level 3+ automation. While automation levels and their operational design domain are gradually evolving in passenger cars and trucks, a more revolutionary introduction of automation is prepared in public transport. Driverless shuttles are being tested at low speeds in semi-public controlled environments under the supervision of safety stewards on board, but will we trust our lives to automated vehicles without steer and pedals and physical human supervision? In a recent survey where shuttle users were not aware of the presence of a safety steward, a majority of users indicated they would not feel safe without any type of human supervision by means of a remote control room or steward on board (Nordhoff et al., 2019).

Electrifying mobility-on-demand (MoD) fleets is an important step towards a more sustainable transportation system. With increasing fleet size, MoD operators will be able to participate in the energy exchange market and will have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of i.e. a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a computationally very efficient model to study the trade-offs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet sizes and different numbers of charging units, we compare the performance of several reactive and scheduling policies. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.

Automated driving will trigger disruptive changes in the transportation system. Automated sharing and pooling options instead of private ownership are broadly discussed because of their possible contributions to climate change mitigation and sustainability. Despite the growing amount of literature on the adoption of these alternatives, little empirical evidence is available on the potential drivers of adoption, such as individuals socioeconomic background, mobility characteristics, attitudes, and values. To address this gap, we utilize the results of an online choice experiment involving 709 participants from Switzerland, which tested future mode choices considering automated cars, automated pooled-use taxis, and automated public transport shuttles, both for short- and long-term mobility decisions. Exploratory regression analysis explains the experiment outcome with a broad set of underlying data predicting willingness to use. Our findings illustrate that automated cars and automated public transport often appeal to the user groups of their traditional non-automated counterparts. However, this does not seem to be the case for automated pooled-use taxis, which we find to be associated with higher-income groups. Attributes on current mobility characteristics and values cannot be significantly associated with automated pooled-use taxis. We also demonstrate that short- and long-term mobility decisions are worth studying together in AV adoption studies.

Electric freight vehicles (EVs) are a sustainable alternative to conventional internal combustion freight vehicles. The driving autonomy of EVs is a fundamental component in the planning of EV routes for goods distribution. In this respect, a complicating factor lies in the fact that EVs' energy consumption is subject to a great deal of uncertainty, which is due to a number of endogenous and exogenous factors. Ignoring such uncertainties in the planning of EV routes may lead a vehicle to run out of energy, which -given the scarcity of recharging stations- may have dire effects. Thus, to foster a widespread use of EVs, we need to adopt new routing strategies that explicitly account for energy consumption uncertainty. In this paper, we propose a new two-stage stochastic programming formulation for the single electric vehicle routing problem with stochastic energy consumption. Furthermore, we develop a decomposition algorithm for this problem. We provide an illustrative example showing the added value of incorporating uncertainty in the route planning process. We perform a variety of computational experiments and show that our decomposition algorithm is capable of efficiently solving instances with 20 customers and 30 scenarios.

Public transport systems, being a safe and sustainable mode of transport, can benefit to a great extent from availing Autonomous vehicle (AV)s. However, the advancements in the road transport infrastructure is not at par with advancements in Intelligent Transportation System (ITS) and vehicle technologies. Cities must consider these changes to realize AVs as a public transport mode. In this paper, we propose three bus-bay designs to integrate AVs with public transport to address this issue. We develop a microscopic traffic simulation model to find the effectiveness of these designs with traffic data obtained from local transport authority and field measurements. The designs are simulated in Singapore city and could be adopted to other cities. Results show that an exclusive AV bay which is physically separated from bus bay reduced the travel time of AV by 5%. Queue length, at the signal near bus stop, has decreased by 27% for such a design.

Autonomous vehicles are believed to change the way we will perceive travel time as the car would no longer be driven by a person, enabling other activities to be performed during the time normally used for controlling the vehicle. Especially for private car commuting, scholars suggest that the value of travel time would decrease substantially due to the possibility to work during the car ride. Travel distance might increase in return, as time used during commuting will not be perceived as time lost. This could lead to the preference of living in rural areas, where land prices and rents are typically lower, and, ultimately, to urban sprawl. By looking at the jobs and mobility characteristics of the Swiss population, we argue that only a small percentage of the total population would actually benefit from active use of travel time during commuting, taking away the fear of urban sprawl through automated vehicles.

Cities are estimated to have a 70% contribution to global greenhouse gas emissions. This makes urban sustainability monitoring necessary, but are urban sustainability monitoring frameworks applicable to cities of all sizes? And do they offer a consistent overview of the sustainability status of core urban development areas, such as transport? The present research tests if the specific needs of small and medium-sized Norwegian cities, as perceived by local stakeholders, are consistently covered by the indicators of urban sustainability monitoring frameworks. To this purpose, four international frameworks were evaluated in the frame of a workshop: Reference Framework for Sustainable Cities, Key Performance Indicators for Smart Sustainable Cities, ISO 37120:2018 Sustainable cities and communities Indicators for city services and quality of life, and LEED for Cities (pilot phase). The evaluation was done by local and regional representatives of academia, the private sector, and public authorities with expertise in urban planning. A set of dedicated transport indicators was also evaluated. The results highlight the alignment between urban and transport sustainability indicators and the perceived needs of Norwegian small and medium-sized cities. These results pave the road for urban sustainability monitoring frameworks to better shape their tools towards the needs of small and medium-sized cities.

The mobility system is undergoing a paradigm shift from fossil fuel-based mobility towards carbon neutrality and greater energy efficiency. Yet this transformation is still in its infancy. In order to reach the CO2 target defined by the Paris Agreement, an increased use of sharing and electric vehicles is suggested. While many scholars have already investigated the factors relevant for promoting the use of sharing or electric vehicles, less is known about the interplay between experience with carsharing and future car buying decisions. We thus adopted a stated choice survey with 995 participants randomly drawn from the German and French-speaking population of Switzerland to test the drivetrain purchase preferences of users with and without carsharing experience. Results suggest that carsharing users are two times more likely to buy an electric-drive vehicle, i.e. battery electric, plug-in hybrid or hybrid electric vehicle, compared to non-carsharing users, even after controlling for socio-demographics, mobility characteristics, values and pro-environmental attitudes.

The main purpose of this work is to develop a single vehicle noise emission model that uses speed as input variable and returns as output a parameter directly referable to the noise source, namely the source sound power level (Lw). The model was tested on three light-duty vehicles with different motorizations: diesel, gasoline and gasoline-electric hybrid. Field measurements were conducted on a straight road and for different speed values (10-90 km/h). The influence of the engaged gear on the noise at different constant speed values was also explored for gasoline and diesel vehicles using one-way analysis of variance (ANOVA). Results revealed that the source power level emitted by different typologies of cars against speed followed significantly different trends, more evident at speeds lower than 40 km/h. In such cases, the contribution of the engine on the noise is prevalent and ANOVA test confirmed that the gear choice influenced the noise at low speeds. At higher speed values such difference disappears.

A new design of a user-centric Emission Trading Systems (ETS) and its implementation as a carbon Blockchain framework for Smart Mobility Data-market (cBSMD) is presented. The cBSMD allows for individual transactions of token-based GHG emission quantities when realizing a trip in a multimodal setting as well as the management of system-wide emission performance data. The cBSMD design is here applied to an ETS framework where individual travellers receive a certain amount of emission credits in the form of tokens. Travellers spend tokens every time they emit GHG when travelling in a multi-modal network through cBSMD transactions. This design instance of cBSMD is then applied to a case-study of 24hours of mobility for 3,187 travelers. The cBSMD performs with a very low latency and high throughput for this number of travelers. To showcase cBSMD data management features, socio-demographic and trip features regarding token usage and emission performance are also analyzed. Our proposed system sets the first implementation step towards the design of future user-centric and practice-ready ETS frameworks.

The application of automation in vehicles has increased rapidly over the years, especially with regard to automated driver support systems. Vehicle automation is proclaimed as a remedy for improved road safety, mobility and cleaner environment. The claim for cleaner environment is based on more efficient driving, so-called “tank to wheel”. What is ignored in this claim is the increase of data required to allow automation. If this data requirement is included and translated into required additional energy production, vehicle automation may jeopardize the environment by causing considerable amounts of additional CO2 and pollutant particle emissions. This article will provide details in the calculation and the magnitude of these effects. Keywords: Automation, data, emission

The era of intelligence transportation is coming. Nonetheless, the transition to an intelligent system will be a gradual process. On the one hand, some zones in the city may be dedicated as autonomous zone with a fully intelligent traffic facility allowing only autonomous vehicles. On the other hand, autonomous and conventional vehicles are both allowed to drive in the remains zone of the network. In this paper, we consider a situation where AVs are deployed by a taxi operating company to serve door-to-door travel requests. Facing this transition period, a flow-based vehicle routing model is developed to determine the optimal fleet size of autonomous and conventional taxis as a function of the gradually increasing coverage of the automated vehicles- only area. Traffic congestion is considered as a dynamically varying travel time with the vehicle flows. In this paper, two service regimes of the company are tested: User Preference Mode (UPM) and System Profit Mode (SPM). The developed model formulations are applied to the case study city of Delft, the Netherlands. The results give insight into the performance of the heterogeneous taxi system on a hybrid network. Strategies are presented on how to adjust the fleet size of autonomous and conventional taxis to get the best system profit while satisfying the mobility demand. The SPM can bring more profit to the operating company by reducing the detour and relocation distance of taxis compared to the UPM.

In the past decade, shared mobility systems involving station-based, dockless and electric bikesharing, and electric scooter sharing (collectively branded as ‘micromobility’ services) have emerged as increasingly popular modes for short trips in urban transportation landscape worldwide. A robust revenue stream is a necessary element of economic sustainability of these services. Bikeshare fare products and pricing plans for casual users (temporary users with no long-term commitment) and members (or subscribers) vary from system to system and change over time. Known as 4Ps of the marketing mix in the marketing parlance, four basic elements of marketing plan, namely: product, price, place and promotion, help develop marketing strategies and tactics (McCarthy, Shapiro & Perrealt 1979). In this research, we conduct a closer scrutiny of reactions of casual users of bikeshare to products (or services) and their pricing. Value-based pricing, which considers customer perspective, increases the likelihood of maximizing revenues from the same set of customers simply by altering their product-selection from the given product mix (Hinterhuber 2008). We define value-based pricing as a strategy for setting the price of a product or service that offers economic value to consumers. The value may be absolute or relative to other products in the choice set and it may be real or perceived. We hypothesize that by introducing value-based pricing options into the fare product mix, micromobility service revenues can be increased thereby enhancing the economic sustainability of the system. We test the hypothesis by conducting a controlled experimental survey of 157 current and potential casual users of bikeshare across six cities in the United States. Respondents’ choices of fare products were registered in two groups – the binary choice set (CS-1) as a control group and the other choice set (CS-2) with an additional value-priced fare as an experimental group.

As evidenced by their rapid adoption in recent years, shared micro-mobility services have resonated with consumers as first and last-mile solutions by penetrating the traditional transportation framework. A recent upsurge in investments from tech industry competitors like Uber, Lyft, Ford, Alphabet, and other venture capital firms points to the likelihood of even more rapid growth in shared micromobility services in the future. Unlike the station-based bike-sharing, which gained steady traction by evolving over a dozen years, e-scooter as a micromobility mode has a recent phenomenon. Often cities are making instant decisions without the benefit of mode choice behaviour. The advent of e-scooters led to the near disappearance of dockless bikesharing system and rapid decline in trips using dock-based bikesharing. Regardless of their mixed reception from the public due to numerous safety concerns, e-scooters outperformed other micromobility services doubling the overall micromobility ridership to 84 million trips in just one year [1]. Very little is known about the adaptable behavior and mode-choice preferences of these growing micromobility users that are critical for policymaking, planning and operations. Several researchers have surveyed users, quantified demand-supply dynamics, modeled user behavior, developed predictive models, and documented noteworthy findings of station-based bikesharing services in the past few years [2]–[5]. These studies addressed user demographics and their mode-choice preferences and the spatial equity of service.

Real-time systems require up-to-date information. Measurement signals in the power train of Electric Vehicles (EVs) are however often received with individual time delays due to the distributed architecture of the power train. Our idea is to compensate the time delays by predicting each signal from the last received value until the present time step. In this work, we evaluate 5 state-of-the-art algorithms and 2 naive methods for time series prediction. We execute all algorithms on real power train data of EVs and compare the results. Our evaluation focuses on run-time and accuracy. All methods achieve a prediction error rate of less than 5 %. As expected, the benchmark naive method is the fastest. Surprisingly, it retrieves comparable results to Exponential Smoothing. BATS and TBATS are the slowest methods. Nevertheless, they achieve the best accuracy, but suffer from outliers. Auto-Regressive Integrated Moving Average (ARIMA) achieves the smallest Mean Absolute Percentage Error (MAPE) and thus the best compromise between outliers and accuracy of all algorithms. Additionally, to further improve the accuracy, we investigate Additionally, to further improve the accuracy, we investigate the benefits of combining predictions of different algorithms.

The design of hybrid electric vehicles based on real-world driving conditions becomes increasingly important due to strict legislation and complexity of future powertrain concepts. Therefore, component sizes should be optimized not only with respect to fuel consumption but also regarding real-world load conditions. Since e.g. a proper design of the cooling system of electrical components requires consideration of time-related power demand, this paper proposes a novel concept of incorporating time frame-based load analysis (TFBA) into driving cycle synthesis. The synthesis is carried out within a two-layer optimization framework where the first layer considers statistical features and the second considers quantities directly related to a vehicles' load implication. By taking into account Markov chain theory, a class frame is derived which then serves as design space for optimizing a sequence of micro-trips with respect to meeting target criteria based on the concepts of mean tractive force and TFBA. To prove practicality, an exemplary driving cycle is synthesized and the method is validated within a parameter study. Results show that the synthetic driving cycle is representative with respect to the considered target criteria and moreover statistical quantities used for validation are within an error tolerance of 5%.

In present study we focus on a special case problem for an island where the transportation is mainly based on barouches pulled by horses due to the limitation on the use of internal combustion engine vehicles. Both the daily travels of islanders and touristic tours of visitors are provided by barouches that are operated as private travel mode, where they serve to meet individual travelers demands. Our motivation has therefore been to alternate barouches with electric vehicles in conjunction with determining the locations of recharging stations in order to respect the animal rights and pollution concerns. Considering the rugged topology of the case area, we notice the limitations and benefits of EVs. On one hand, noticing the slope climbing restrictions and limited driving range of EVs may be accepted as the most significant challenges of alternating the vehicle types of the fleet. On the other hand, the potential of regenerative energy, together with the reduced emission effects, is a promising feature of EVs. In this context, we formulate an Electrified Location Routing Problem using mixed integer linear programming by realistically calculating the battery load considering explicitly the driving resistances and the potential regenerative braking.

This paper proposes a model for charge scheduling of electric vehicles in last-mile distribution that takes into account battery degradation. A mixed integer linear programming formulation is proposed that minimizes labor, battery degradation and time-dependent energy costs. The benefit of implementing charge schedule optimization is assessed for a real-life case study at e-grocer Picnic. It is shown that charging optimization yields an overall reduction of charging costs by 25.2% when compared to the current operational charging performance. Furthermore, the impacts of three different shift schedule types, the increase in vehicle battery size and the coordinated charging are investigated. It turns out that more energy demanding shift schedules result in higher average charging cost per charged amount of energy. The introduction of a larger battery size as well as coordinated charging show potential for decreasing overall costs.

The dynamic origin-destination (OD) information is crucial for traffic operations and control. This paper presents a dynamic traffic demand prediction framework based on an anomaly detection algorithm. The Principal Component Analysis (PCA) method is applied to extract main demand patterns which are used to detect the abnormal conditions. The proposed approach can select prediction methods (parametric or nonparametric) automatically based on the pattern detection results. Both simulation and field observed Automatic Number Plate Recognition (ANPR) data are used to verify the proposed approach where the Kalman filter model and the K-nearest neighbor model are chosen as the basic prediction methods. The results show that the prediction framework can effectively reduce the noise of a single prediction model particularly in the abnormal conditions and provide more accurate and reliable prediction results.

AbstractThe Yangtze River is the first of China and the third-longest river in the world. It is the most developed inland water transportation system in China. Chongqing, located in the upstream of the Three Gorges Project, is not only the gateway of the industrial and commercial center of China but also the most abundant water and intermodal inland hub and material distribution center in the southwest. Frequent water traffic accidents pose severe threats to the safety of human life and property and the water environment as well as bring adverse effects on social stability. Therefore, based on the ten years of statistical data of ship accidents in 2009-2018 from the Chongqing Maritime Safety Administration (MSA), this paper summarizes the characteristics of the spatiotemporal distribution of accidents through statistical analysis of the historical data. Moreover, it proposes accident prevention and supervision methods to provide decision support for maritime safety. This research is of great significance to the prevention and control of water traffic accidents in this region. Index TermsShip accidents, statistical analysis, water transportation, risk analysis, data visualization, maritime safety.

1. Problem statement and contribution E-commerce is a rapidly growing and evolving sector. The sector is however struggling with its organization of the last-mile deliveries in order that it meets the sustainability requirements, both economically and environmentally. Different delivery methods translate into different environmental impacts. Several studies have established the advantages of collection points in terms of air pollution and/or CO2 emissions in comparison to home delivery (McLeod et al., 2006; McLeod and Cherrett, 2009; Song et al., 2009; Edwards et al., 2010; Giuffrida et al., 2016; Carotenuto et al., 2018). Zhang et al. (2018) and Verlinde et al. (2019) reach similar conclusions but only given a specific time of consumer behavior, identifying consumers’ collection trips as determining factor in comparing the environmental impact. Yet, these studies all apply for urban areas. However, consumers of all places (also suburban and rural) engage in online retailing. Moreover, web-shops hardly focus on urban inhabitants solely and often take a national approach. Given that the results of available research is bound to the local/urban context, their conclusions cannot be generalized or transferred to rural or suburban areas. 2. Methodology Following our objective to address this gap, we investigated the environmental impact associated with four last mile delivery methods from the perspective of non-food retail products in Belgium. We compared deliveries originating from a dedicated distribution center to homes (direct with 3PL or via dedicated local distribution centers) and collection points (direct or via store supply) in terms transport-related external costs for CO2 emissions, air polluting emissions, accidents, noise nuisance, infrastructure and congestion and assess the differences in impact between consumers’ residences, specifically urban, urbanized and rural areas. Both logistics flows and customer movements to the collection points were considered. To do so we applied the agent-based simulation model TRABAM (Mommens, 2019). The model uses MATSim and extends the Freight Extension (Schröder et al., 2012). 3. Results The results indicate on the one hand that homedeliveries via a well-established 3PL (with high daily volume i.e. 250.000 parcels) is the most sustainable scenario for urban, urbanized and rural areas. Deliveries to collection points are in none of the considered scenarios more sustainable than homedeliveries via 3PL, this is due to the customer movements. Yet it has to be noted that the difference between homedeliveries via 3PL and deliveries to collection points via store supply is very small to almost neglectable for the urban area. The difference becomes bigger for urbanized area and is the biggest for the rural area. Organizing a proper homedelivery system is not a sustainable option for the considered case. On the other hand, significant differences (between 10 and 15%) in terms of sustainability were found within scenario’s between urban, urbanized and rural areas. Both indicate that the environment (urban, urbanized, rural) should be considered as a parameter.

In this paper we present a methodology to more accurately calculate emissions in regional freight transportation models by taking the loading rate of vehicles into account in the emission factors. This is done by incorporating the shipment-based demand data from a multi-agent simulation framework into the route assignment. In a case study we show that disregarding the vehicle loading rate can lead to underestimations of the amount of pollutant gasses. The application of this methodology in combination with a multi-agent simulation model allows to simulate impacts of changes in logistic behavior, such as vehicle type use or the scheduling of round tours, on emissions more accurately.

The paper presents a model to estimate the demand of parcels by disaggregating nation-wide commodity flows. Secondly, the model generates parcel delivery tours to quantify transport-related effects. This model is applied to simulate parcel deliveries using cargo bikes. It consists of a two-step process: first, micro depots located close to the demand are fed with vans; second, parcels from micro depots to the customers are distributed by cargo bikes. The model simulates different shares of cargo bikes vs. motorized vans to deliver the same demand. We also studied the effect of micro depot densities and different parcel demand intensities in the same catchment area. Lastly, we compared the cargo bike tours at locations with different demand densities (parcels/km2). The results find beneficial effects of cargo bikes when the demand density and the share of cargo bikes is high. Under these conditions, the total vehicle-kilometer traveled and motorized vehicle emissions can be reduced.

Shared e-mobility is a category of emerging mobility services that includes electric carsharing, e-bike sharing and e-scooter sharing. These services are expected to reduce the negative externalities of road transport that is currently dominated by fossil-fuel-powered private car trips. In order to better inform the development and promotion of these services and indicate directions for further research, we conduct a comprehensive review of existing literature on the three shared e-mobility modes focusing on their usage pattern, demand estimation and potential impacts. We found that despite the different vehicle capabilities, all three shared e-mobility services are mainly used for short trips, and their current users are mostly male, middle-aged people with relatively high income and education. The demands of all shared e-mobility modes share many common predictors: they appeal to people with similar socio-demographic characteristics and generate higher demand in locations with better transport connection and more point of interests. Shared e-mobility services can potentially lead to positive impacts on transportation and the environment, such as reducing car use, car ownership, and greenhouse gas emissions. However, the magnitude of these benefits depends on the specific operating conditions of the services such as the fuel type and lifetime of shared vehicles. The impact of each shared e-mobility mode is also expected to be affected by other coexisting shared e-mobility modes due to both complementarity and competition. Future directions should include studying the competition between and integration of multiple shared e-mobility modes and the effect of automation.

In this work, we develop different scenarios regarding the initiatives and incentives to foster the use of electric vehicles (EVs) and test the different results on an optimal allocation tool for charging stations (ETCharger [1]) in The Hague city, the Netherlands. This is a sub-task from project ELECTRIC TRAVELLING [2]. Firstly, we overview the initiatives and incentives that have been implemented to foster the use of EVs in different regions in Europe [3] and analyzes the incentives that will be used in the implementation of the project to foster electromobility (e-mobility) in smart cities. Then the database of the city The Hague including existing transport network, and transport infrastructure parameters, especially the ones related to e-mobility etc., are introduced in order to apply the proposed tool to the case study. Based on the analysis focused on the incentives, we develop different scenarios of the transport system and use them to allocate charging stations. A comparison is done between different scenarios to select the optimal station locations according to the main goal of the project: foster increase the number of travellers by using EVs.

Air pollution and in particular PM2.5 emissions are a major problem worldwide. Road transport is a significant contributor to PM2.5 emissions in urban areas and as such it is important to understand and be able to accurately model the effects of vehicles on PM2.5 emissions. In this paper a computer vision algorithm is introduced which is able to extract vehicle trajectories from video footage. The algorithm has a 100% accuracy for overall total vehicle counting. Comparing the speeds predicted by the computer vision script to manually following a single vehicle feature on the video file, the average relative speed accuracy is 2.7% at a 1 Hz time resolution. Using these vehicle trajectories in an instantaneous vehicle emissions model and also as input to COPERT v5, tailpipe PM2.5 emissions were estimated and compared to on-road measurements. It was shown that a local sensor is not sufficient to determine vehicle tailpipe emissions due to the influence of meteorological conditions and other emission sources. Combining computer vision with an instantaneous vehicle emissions model is a useful method to evaluate changes in emissions caused by transport policies.

Many alternative approaches to achieve improvement in transportation system sustainability are continuously being proposed. The sheer number and diversity of suggested approaches available in the literature is overwhelming. Decision makers, consequently, need support in the fair and comprehensive evaluation, comparison, and combination of these proposed initiatives. A methodology for the generation of scientific-based decision support on the development of truly integrated strategies to improve transport sustainability is presented in this paper. The key contribution of the approach produced is that all the underlying complexities in the formulation of such strategies are adequately addressed and incorporated in its design. This is achieved through the development of a customized multi-objective metaheuristic simulation optimization model, called the Transport Energy Management Tool (TEMT). A case study application of the TEMT is used to demonstrate its success and usefulness and serves as a proof of concept for adoption of this approach in sustainable transport management.

Although the electrification of transportation can bring long-term sustainability, increasing penetration of Electric Vehicles (EVs) may cause more congestion. Inappropriate deployment of charging stations not only hinders the EVs adoption but also increases the total system costs. This paper attempts to identify the optimal locations for fast-charging stations in the urban network considering heterogeneous vehicles with respect to the traffic congestion at different levels of EVs' penetration. A bi-level optimization framework is proposed to solve this problem in which the upper level aims to locate charging stations by minimizing the total travel time and the infrastructure costs. On the other hand, the lower level captures re-routing behaviors of travelers with their driving ranges. Finally, numerical study is performed to demonstrate the fast convergence of the proposed framework.

Every year, some 4.5 million containers are being discharged in ports around the Netherlands. Most containers arrive on seagoing vessels in the Ports of Rotterdam and Amsterdam, from where they continue their journey on inland vessels, trains and heavy goods vehicles (HGVs) towards their destination, either within the Netherlands or elsewhere in Europe. To date, there is no complete overview of all this container transport. Statistics Netherlands (CBS) is studying possible ways of mapping the container routes within the country from beginning to end in a pilot study commissioned by Rijkswaterstaat (Department of Waterways and Public Works in the Netherlands, tr.) Essential elements in the process are new data from transport companies and customs data. Incomplete data CBS keeps track of both goods flows and transport flows through the Netherlands. The statistics on freight transport are broken down by mode: maritime and inland navigation, railway and road transport. For certain types of goods such as bulk commodities – e.g. coal and ore – this yields a fairly accurate picture. Bulk commodities tend to be carried by one single mode of transport (unimodal). However, the breakdown is less suited for other types of cargo such as containers: ‘Containers are often transhipped, moving from one mode to another,’ explains Mathijs Jacobs, traffic and transport researcher at CBS. ‘When you look at container transport broken down by mode, the data on transhipment movements is limited and there is a risk of double counting goods in the overall picture. Source data for the freight transport statistics include registers and surveys based on sampling, but these are incomplete. We fill the information gaps with estimation models. These are based on assumptions and they don’t cover the entire chain of freight transport. Especially data on individual container loads and the routes they travel are lacking.’

Analyzing and visualizing traffic data in order to better understand congestion trends, safety concerns, goods movement and capacity needs is a pressing need. Broadly speaking, there is a large amount of traffic data available today, including volume, lane occupancy, speed, and travel time, which can be used to manage transportation networks, provide traveler information and produce performance measures. This broadly disseminated data almost always treats all vehicles alike, without discriminating between trucks and passenger cars. Since trucks are critical and growing components of freeway traffic, monitoring and tracking their dynamics can reveal the impacts of freight movement on current freeway operations and over time will uncover trends useful for future planning and management. This study takes advantage of a unique data stream available for the freeway network of Portland, Oregon, USA. In addition to providing continuous vehicle count, speed and lane occupancies at 20-second intervals at more than 500 stations (1,300 individual detectors), the Portland system reports volume bins at 4 length-based classifications (< 20, 20-35, 36-30, >60 ft). Given that most vehicle classification studies are done over very short time intervals at an extremely limited number of locations, this nonstop data stream enables unprecedented insight into where and when trucks are traveling on Portlands freeways and a wealth of opportunities for performance measurement and diagnosis of their impacts. The objective of this paper is to exploit this new data stream and explore new visualization techniques that depict truck volume, truck percentage and volume-weighted average vehicle speeds along Portlands Interstate 5 corridor, an important north-south freight route between the Canada and

Autonomous vehicles present opportunities for highly integrated multi-modal urban mobility services. This study reports on the evaluation of autonomous services that can be integrated with the existing public transport network. The autonomous services considered are first/last mile feeder services, on-demand point-to-point services, robo-taxis, and autonomous car-sharing and Bus Rapid Transit. In this evaluation, the views of users, public transport operators, public transport authorities and mobility service providers are taken into account. An in-depth understanding of the objectives for each stakeholder group is needed in order to assess the impact of new mobility services. Representatives from each stakeholder group were consulted to evaluate autonomous vehicle scenarios. Using the multi-actor multi-criteria analysis method, stakeholder criteria were weighted and used to calculate overall performance scores per scenario. The results indicate that users are positive towards all autonomous scenarios. For public transport operators all scenarios, except car-sharing, perform well. Public transport authorities believe more strongly in the benefits of on-demand point-to-point services, first/last mile feeders and bus rapid transport. Mobility service providers value flexible services most. These insights can be applied to evaluate the business models of public transport operators and mobility service providers and used to shape urban transport policies.

Acceptance is often claimed to be the crucial factor for autonomous vehicle success. In this study, we examine the factors that influence the acceptance of autonomous transit feeders using a hybrid choice model. Our research is based on data from a Germany-wide online survey with more than 1,000 respondents. The influence of gender, travel behavior, individual availability of mobility tools, such as car ownership or transit pass, and previous knowledge coincides with findings from previous studies. Further, we are able to explain a large part of the unexplained heterogeneity compared to a base ordered probit model with the latent variables simplification through autonomous minibuses and pro-transit-attitude. This indicates the relevance of considering attitudes in future research on the acceptance of autonomous vehicles in order to arrive at correct interpretation and to reduce the heterogeneity in predicting models.

This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.

Every day we hear about the current state of the climate and how it is degrading very quickly. It is urgent to study ways in which people can change and improve their daily lives towards more sustainable habits. This paper studies graph-based simulation scenarios, focusing on both human and infrastructure characteristics, which could lead to a potential reduction in CO2 emissions. We compare alternative policies with an upper-bound base scenario, in which people use their own cars for their sole use. Most of these scenarios are based on Dijkstras shortest path algorithm, while others serve to underlie our reflections on the feasibility of using minimal spanning trees to solve the problem. The zone selected to illustrate this study is the coastal area of Espinho, a city in Portugal. It is shown that the scenarios with carpooling starting on the cut vertices of a tree generated by linking Dijkstras shortest paths of each agent yield the best results. This approach leads to a reduction in carbon emissions of between 25% and 30%.

eHUBS are on-street locations that bring together e-bikes, e-cargo bikes, e-scooters and/or e-cars, offering users a wide range of options to experiment and use in various situations. The idea is to give a high-quality and diverse offer of shared electric mobility services to dissuade citizens from owning private cars, resulting in a cleaner, more liveable and pleasant cities. The eHUBS project has worked together closely with the TOMP-API Working Group on a technical standard interface for communication between Transport Operators and MaaS Providers while keeping the needs of the public authorities and cities in close consideration. The TOMP-API allows all participating companies to communicate about planning, booking, execution, support, general information and payments of multimodal, end-user specific trips. This TOMP-API environment ensures growth in opportunities for interoperability between multiple parties. The presentation is going to show the importance of this standardisation and the power of the TOMP-API in optimizing data flows between Transport Operators and Maas Providers. Within the eHUBS project, we have created a kiosk application that presents travellers with a clear and understandable overview of (realtime) travel options & information within the direct vicinity of the eHUB, even when they don’t have access to a smartphone. This is achieved by collecting and interpreting data of the Transport Operators providing their services nearby the eHUB point, of course in close cooperation with the local authorities. Because of the local character, the data-sources and quality differ immensely. Some hubs are in densely connection urban areas, but they can also be situated in more rural surroundings. This application is what will be used to illustrate the strength of the TOMP-API, it will show how easy and fast it can be to implement new transport options and make it available directly. Especially when compared to the tedious process of combing to endless API descriptions and data-formats trying to find some unity and ways to reliably incorporate this. Supporting local Transport Operators in adopting this standard will be an important step forward for the digitalisation of the infrastructure in an effective and sustainable manner. The presentation will be hands-on and despite its technical nature, accessible and understandable for a broad audience.

This paper aims to optimize on-ramp merging processes for connected automated vehicles by utilizing an existing hierarchical control architecture including a decision-maker and an operational controller. The decision-maker employs surrogate linear models to predict future vehicular acceleration analytically and computes a merging sequence to minimize merging times of on-ramp vehicles. The operational controller is formulated as a model predictive control problem, which utilizes a second-order vehicle dynamics model, and regulates vehicles accelerations and time instants to execute lateral movements of on-ramp vehicles for the merging processes respectively. Constraints on vehicular acceleration, speed, and inter-vehicle distance are considered by the decision-maker and the operational controller for practical usage. The proposed method to minimize the merging times of on-ramp vehicles and a first-in-first-out method are tested under different initial settings, including initial vehicular speeds, distributions of vehicular positions, and desired time gaps. The simulation results show that the proposed method is superior to the first-in-first-out method widely used in literature in improving merging traffic efficiency. We find that cooperation among vehicles makes the on-ramp vehicles join mainline traffic faster, and the acceptable time gap for merging affect choices of optimal merging sequences.

Autonomous vehicles (AVs) occupy a crucial part of the emerging mobility. Currently, the potential impacts of self-driving fleets are of great interest to public bodies and authorities. The impact is studied in pilots, but these still have a limited scope, therefore also simulation studies are necessary. This study aims to offer a comprehensive review of recent macro/mesoscopic agent-based models for AVs. Through keyword search, we extracted twenty-nine papers from the Web of Science database. These studies analysed AVs impact on the land-use, travel behaviour, environment and cost. Therefore, we summarise the similarities and particularities of modelling specifications and outcomes, meanwhile analysing the reasons for conflicting results. In general, the results from analysed papers are distinct from each other, and some even hold opposite results. There are several effective methods for increasing AVs customer acceptance. When implemented, it will bring significant amounts of benefit, including much lower energy consumptions, emission, operational costs and parking space demands. However, a more congested network and urban sprawl are the most unignorable problems. Finally, this research offers several suggestions and research directions regarding AV future development, which will be referential for both researchers and urban planners.

AbstractThis paper proposes a multilane traffic flow model based on the notions of conservation laws in Lagrangian coordinates. Both the mathematical formulation and the graphical representation are provided. A logit choice model is applied to describe drivers lane choice probability. The lane changing rate is estimated by employing the Incremental Transfer (IT) principle and the lane choice probability model. The numerical implementation of the model in the case of two lanes is discussed. The simulation results reveal that the proposed Lagrangian model is able to describe lane changing dynamics of vehicle platoons; while the lane changing equilibrium curve at the macroscopic level is consistent with that from the multilane Eulerian model as well as the observed data on the highway.

Many railway companies are experimenting with Automatic Train Operation (ATO) and most of them are aiming at grade of automation (GoA) level 2. This is the automation level whereas the train is driven automatically, and the driver is still fully responsible and also performing other tasks. The field of Human Factors studies the interaction between human beings and technology in order to optimize the performance delivered by this team. Human factors are a significant aspect in designing and improving the ATO implementation. Netherlands Railways (NS) has started experiments with ATO GoA2 in 2019. One of the main questions was how drivers felt in their cooperation with ATO. Our aim was to discover the (possible) interactions between ATO and drivers, to be able to evaluate and improve their cooperation and resulting performance. We know that people tend to forget important details when time evolves (e.g. Ebbinghaus forgetting curve). Our methodological question is how we could log the details of (possible) interactions precisely. Furtheron, we also want to create an anchor point such that drivers can remember the interaction again and is able to elaborate on the details. We came across the customer experience journey and noticed the similarities with the questions we want to be answered. Customer experience involves a constant feedback loop repeated throughout the usage lifecycle including from initial discovery through purchase, out-of-box, usage, maintenance, upgrades, and disposal (Beauregard et al. 2007). The customer experience consists of perceptions that shape emotions, thoughts, and attitudes. The customer journey maps direct and indirect touchpoints between customer and process, and indicates per touchpoint the emotions associated (Schmitt 1999). The customer journey comes from service design.

Since the early 19th century, we trust our lives in cars driving at high speeds in complex traffic. Trust in vehicle technology is justified since the vehicle design and production process has resulted in very high-reliability levels. Vehicle automation technology has not yet reached a level of maturity that is comparable to vehicle functioning in manual driving. A range of today’s passenger cars provides SAE Level 2 automation through Adaptive Cruise Control (ACC) and Lane Centring Systems, requiring the permanent supervision of human drivers to ensure the reliable and safe operation of the automated driving system (SAE, 2018). Drivers shall be aware of the limitations of Level 2 automation and shall not overtrust automation as this will lead to misuse and will threaten road safety. Higher automation levels (SAE Level 3+) gradually reduce the level of supervision of human drivers, allowing human drivers to direct attention away from the driving task and the supervision of the performance of the automated driving system to the engagement in eyes-off road activities. The efficient, comfortable and safe use of these higher automation systems requires high levels of trust in and perceived safety of vehicle automation technology. Hence undertrust can become a bottleneck in acceptance of Level 3+ automation. While automation levels and their operational design domain are gradually evolving in passenger cars and trucks, a more revolutionary introduction of automation is prepared in public transport. Driverless shuttles are being tested at low speeds in semi-public controlled environments under the supervision of safety stewards on board, but will we trust our lives to automated vehicles without steer and pedals and physical human supervision? In a recent survey where shuttle users were not aware of the presence of a safety steward, a majority of users indicated they would not feel safe without any type of human supervision by means of a remote control room or steward on board (Nordhoff et al., 2019).

Electrifying mobility-on-demand (MoD) fleets is an important step towards a more sustainable transportation system. With increasing fleet size, MoD operators will be able to participate in the energy exchange market and will have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of i.e. a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a computationally very efficient model to study the trade-offs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet sizes and different numbers of charging units, we compare the performance of several reactive and scheduling policies. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.

Automated driving will trigger disruptive changes in the transportation system. Automated sharing and pooling options instead of private ownership are broadly discussed because of their possible contributions to climate change mitigation and sustainability. Despite the growing amount of literature on the adoption of these alternatives, little empirical evidence is available on the potential drivers of adoption, such as individuals socioeconomic background, mobility characteristics, attitudes, and values. To address this gap, we utilize the results of an online choice experiment involving 709 participants from Switzerland, which tested future mode choices considering automated cars, automated pooled-use taxis, and automated public transport shuttles, both for short- and long-term mobility decisions. Exploratory regression analysis explains the experiment outcome with a broad set of underlying data predicting willingness to use. Our findings illustrate that automated cars and automated public transport often appeal to the user groups of their traditional non-automated counterparts. However, this does not seem to be the case for automated pooled-use taxis, which we find to be associated with higher-income groups. Attributes on current mobility characteristics and values cannot be significantly associated with automated pooled-use taxis. We also demonstrate that short- and long-term mobility decisions are worth studying together in AV adoption studies.

Electric freight vehicles (EVs) are a sustainable alternative to conventional internal combustion freight vehicles. The driving autonomy of EVs is a fundamental component in the planning of EV routes for goods distribution. In this respect, a complicating factor lies in the fact that EVs' energy consumption is subject to a great deal of uncertainty, which is due to a number of endogenous and exogenous factors. Ignoring such uncertainties in the planning of EV routes may lead a vehicle to run out of energy, which -given the scarcity of recharging stations- may have dire effects. Thus, to foster a widespread use of EVs, we need to adopt new routing strategies that explicitly account for energy consumption uncertainty. In this paper, we propose a new two-stage stochastic programming formulation for the single electric vehicle routing problem with stochastic energy consumption. Furthermore, we develop a decomposition algorithm for this problem. We provide an illustrative example showing the added value of incorporating uncertainty in the route planning process. We perform a variety of computational experiments and show that our decomposition algorithm is capable of efficiently solving instances with 20 customers and 30 scenarios.

Public transport systems, being a safe and sustainable mode of transport, can benefit to a great extent from availing Autonomous vehicle (AV)s. However, the advancements in the road transport infrastructure is not at par with advancements in Intelligent Transportation System (ITS) and vehicle technologies. Cities must consider these changes to realize AVs as a public transport mode. In this paper, we propose three bus-bay designs to integrate AVs with public transport to address this issue. We develop a microscopic traffic simulation model to find the effectiveness of these designs with traffic data obtained from local transport authority and field measurements. The designs are simulated in Singapore city and could be adopted to other cities. Results show that an exclusive AV bay which is physically separated from bus bay reduced the travel time of AV by 5%. Queue length, at the signal near bus stop, has decreased by 27% for such a design.

Autonomous vehicles are believed to change the way we will perceive travel time as the car would no longer be driven by a person, enabling other activities to be performed during the time normally used for controlling the vehicle. Especially for private car commuting, scholars suggest that the value of travel time would decrease substantially due to the possibility to work during the car ride. Travel distance might increase in return, as time used during commuting will not be perceived as time lost. This could lead to the preference of living in rural areas, where land prices and rents are typically lower, and, ultimately, to urban sprawl. By looking at the jobs and mobility characteristics of the Swiss population, we argue that only a small percentage of the total population would actually benefit from active use of travel time during commuting, taking away the fear of urban sprawl through automated vehicles.

Cities are estimated to have a 70% contribution to global greenhouse gas emissions. This makes urban sustainability monitoring necessary, but are urban sustainability monitoring frameworks applicable to cities of all sizes? And do they offer a consistent overview of the sustainability status of core urban development areas, such as transport? The present research tests if the specific needs of small and medium-sized Norwegian cities, as perceived by local stakeholders, are consistently covered by the indicators of urban sustainability monitoring frameworks. To this purpose, four international frameworks were evaluated in the frame of a workshop: Reference Framework for Sustainable Cities, Key Performance Indicators for Smart Sustainable Cities, ISO 37120:2018 Sustainable cities and communities Indicators for city services and quality of life, and LEED for Cities (pilot phase). The evaluation was done by local and regional representatives of academia, the private sector, and public authorities with expertise in urban planning. A set of dedicated transport indicators was also evaluated. The results highlight the alignment between urban and transport sustainability indicators and the perceived needs of Norwegian small and medium-sized cities. These results pave the road for urban sustainability monitoring frameworks to better shape their tools towards the needs of small and medium-sized cities.

The mobility system is undergoing a paradigm shift from fossil fuel-based mobility towards carbon neutrality and greater energy efficiency. Yet this transformation is still in its infancy. In order to reach the CO2 target defined by the Paris Agreement, an increased use of sharing and electric vehicles is suggested. While many scholars have already investigated the factors relevant for promoting the use of sharing or electric vehicles, less is known about the interplay between experience with carsharing and future car buying decisions. We thus adopted a stated choice survey with 995 participants randomly drawn from the German and French-speaking population of Switzerland to test the drivetrain purchase preferences of users with and without carsharing experience. Results suggest that carsharing users are two times more likely to buy an electric-drive vehicle, i.e. battery electric, plug-in hybrid or hybrid electric vehicle, compared to non-carsharing users, even after controlling for socio-demographics, mobility characteristics, values and pro-environmental attitudes.

The main purpose of this work is to develop a single vehicle noise emission model that uses speed as input variable and returns as output a parameter directly referable to the noise source, namely the source sound power level (Lw). The model was tested on three light-duty vehicles with different motorizations: diesel, gasoline and gasoline-electric hybrid. Field measurements were conducted on a straight road and for different speed values (10-90 km/h). The influence of the engaged gear on the noise at different constant speed values was also explored for gasoline and diesel vehicles using one-way analysis of variance (ANOVA). Results revealed that the source power level emitted by different typologies of cars against speed followed significantly different trends, more evident at speeds lower than 40 km/h. In such cases, the contribution of the engine on the noise is prevalent and ANOVA test confirmed that the gear choice influenced the noise at low speeds. At higher speed values such difference disappears.

A new design of a user-centric Emission Trading Systems (ETS) and its implementation as a carbon Blockchain framework for Smart Mobility Data-market (cBSMD) is presented. The cBSMD allows for individual transactions of token-based GHG emission quantities when realizing a trip in a multimodal setting as well as the management of system-wide emission performance data. The cBSMD design is here applied to an ETS framework where individual travellers receive a certain amount of emission credits in the form of tokens. Travellers spend tokens every time they emit GHG when travelling in a multi-modal network through cBSMD transactions. This design instance of cBSMD is then applied to a case-study of 24hours of mobility for 3,187 travelers. The cBSMD performs with a very low latency and high throughput for this number of travelers. To showcase cBSMD data management features, socio-demographic and trip features regarding token usage and emission performance are also analyzed. Our proposed system sets the first implementation step towards the design of future user-centric and practice-ready ETS frameworks.

The application of automation in vehicles has increased rapidly over the years, especially with regard to automated driver support systems. Vehicle automation is proclaimed as a remedy for improved road safety, mobility and cleaner environment. The claim for cleaner environment is based on more efficient driving, so-called “tank to wheel”. What is ignored in this claim is the increase of data required to allow automation. If this data requirement is included and translated into required additional energy production, vehicle automation may jeopardize the environment by causing considerable amounts of additional CO2 and pollutant particle emissions. This article will provide details in the calculation and the magnitude of these effects. Keywords: Automation, data, emission

The era of intelligence transportation is coming. Nonetheless, the transition to an intelligent system will be a gradual process. On the one hand, some zones in the city may be dedicated as autonomous zone with a fully intelligent traffic facility allowing only autonomous vehicles. On the other hand, autonomous and conventional vehicles are both allowed to drive in the remains zone of the network. In this paper, we consider a situation where AVs are deployed by a taxi operating company to serve door-to-door travel requests. Facing this transition period, a flow-based vehicle routing model is developed to determine the optimal fleet size of autonomous and conventional taxis as a function of the gradually increasing coverage of the automated vehicles- only area. Traffic congestion is considered as a dynamically varying travel time with the vehicle flows. In this paper, two service regimes of the company are tested: User Preference Mode (UPM) and System Profit Mode (SPM). The developed model formulations are applied to the case study city of Delft, the Netherlands. The results give insight into the performance of the heterogeneous taxi system on a hybrid network. Strategies are presented on how to adjust the fleet size of autonomous and conventional taxis to get the best system profit while satisfying the mobility demand. The SPM can bring more profit to the operating company by reducing the detour and relocation distance of taxis compared to the UPM.

In the past decade, shared mobility systems involving station-based, dockless and electric bikesharing, and electric scooter sharing (collectively branded as ‘micromobility’ services) have emerged as increasingly popular modes for short trips in urban transportation landscape worldwide. A robust revenue stream is a necessary element of economic sustainability of these services. Bikeshare fare products and pricing plans for casual users (temporary users with no long-term commitment) and members (or subscribers) vary from system to system and change over time. Known as 4Ps of the marketing mix in the marketing parlance, four basic elements of marketing plan, namely: product, price, place and promotion, help develop marketing strategies and tactics (McCarthy, Shapiro & Perrealt 1979). In this research, we conduct a closer scrutiny of reactions of casual users of bikeshare to products (or services) and their pricing. Value-based pricing, which considers customer perspective, increases the likelihood of maximizing revenues from the same set of customers simply by altering their product-selection from the given product mix (Hinterhuber 2008). We define value-based pricing as a strategy for setting the price of a product or service that offers economic value to consumers. The value may be absolute or relative to other products in the choice set and it may be real or perceived. We hypothesize that by introducing value-based pricing options into the fare product mix, micromobility service revenues can be increased thereby enhancing the economic sustainability of the system. We test the hypothesis by conducting a controlled experimental survey of 157 current and potential casual users of bikeshare across six cities in the United States. Respondents’ choices of fare products were registered in two groups – the binary choice set (CS-1) as a control group and the other choice set (CS-2) with an additional value-priced fare as an experimental group.

As evidenced by their rapid adoption in recent years, shared micro-mobility services have resonated with consumers as first and last-mile solutions by penetrating the traditional transportation framework. A recent upsurge in investments from tech industry competitors like Uber, Lyft, Ford, Alphabet, and other venture capital firms points to the likelihood of even more rapid growth in shared micromobility services in the future. Unlike the station-based bike-sharing, which gained steady traction by evolving over a dozen years, e-scooter as a micromobility mode has a recent phenomenon. Often cities are making instant decisions without the benefit of mode choice behaviour. The advent of e-scooters led to the near disappearance of dockless bikesharing system and rapid decline in trips using dock-based bikesharing. Regardless of their mixed reception from the public due to numerous safety concerns, e-scooters outperformed other micromobility services doubling the overall micromobility ridership to 84 million trips in just one year [1]. Very little is known about the adaptable behavior and mode-choice preferences of these growing micromobility users that are critical for policymaking, planning and operations. Several researchers have surveyed users, quantified demand-supply dynamics, modeled user behavior, developed predictive models, and documented noteworthy findings of station-based bikesharing services in the past few years [2]–[5]. These studies addressed user demographics and their mode-choice preferences and the spatial equity of service.

Real-time systems require up-to-date information. Measurement signals in the power train of Electric Vehicles (EVs) are however often received with individual time delays due to the distributed architecture of the power train. Our idea is to compensate the time delays by predicting each signal from the last received value until the present time step. In this work, we evaluate 5 state-of-the-art algorithms and 2 naive methods for time series prediction. We execute all algorithms on real power train data of EVs and compare the results. Our evaluation focuses on run-time and accuracy. All methods achieve a prediction error rate of less than 5 %. As expected, the benchmark naive method is the fastest. Surprisingly, it retrieves comparable results to Exponential Smoothing. BATS and TBATS are the slowest methods. Nevertheless, they achieve the best accuracy, but suffer from outliers. Auto-Regressive Integrated Moving Average (ARIMA) achieves the smallest Mean Absolute Percentage Error (MAPE) and thus the best compromise between outliers and accuracy of all algorithms. Additionally, to further improve the accuracy, we investigate Additionally, to further improve the accuracy, we investigate the benefits of combining predictions of different algorithms.

The design of hybrid electric vehicles based on real-world driving conditions becomes increasingly important due to strict legislation and complexity of future powertrain concepts. Therefore, component sizes should be optimized not only with respect to fuel consumption but also regarding real-world load conditions. Since e.g. a proper design of the cooling system of electrical components requires consideration of time-related power demand, this paper proposes a novel concept of incorporating time frame-based load analysis (TFBA) into driving cycle synthesis. The synthesis is carried out within a two-layer optimization framework where the first layer considers statistical features and the second considers quantities directly related to a vehicles' load implication. By taking into account Markov chain theory, a class frame is derived which then serves as design space for optimizing a sequence of micro-trips with respect to meeting target criteria based on the concepts of mean tractive force and TFBA. To prove practicality, an exemplary driving cycle is synthesized and the method is validated within a parameter study. Results show that the synthetic driving cycle is representative with respect to the considered target criteria and moreover statistical quantities used for validation are within an error tolerance of 5%.

In present study we focus on a special case problem for an island where the transportation is mainly based on barouches pulled by horses due to the limitation on the use of internal combustion engine vehicles. Both the daily travels of islanders and touristic tours of visitors are provided by barouches that are operated as private travel mode, where they serve to meet individual travelers demands. Our motivation has therefore been to alternate barouches with electric vehicles in conjunction with determining the locations of recharging stations in order to respect the animal rights and pollution concerns. Considering the rugged topology of the case area, we notice the limitations and benefits of EVs. On one hand, noticing the slope climbing restrictions and limited driving range of EVs may be accepted as the most significant challenges of alternating the vehicle types of the fleet. On the other hand, the potential of regenerative energy, together with the reduced emission effects, is a promising feature of EVs. In this context, we formulate an Electrified Location Routing Problem using mixed integer linear programming by realistically calculating the battery load considering explicitly the driving resistances and the potential regenerative braking.

This paper proposes a model for charge scheduling of electric vehicles in last-mile distribution that takes into account battery degradation. A mixed integer linear programming formulation is proposed that minimizes labor, battery degradation and time-dependent energy costs. The benefit of implementing charge schedule optimization is assessed for a real-life case study at e-grocer Picnic. It is shown that charging optimization yields an overall reduction of charging costs by 25.2% when compared to the current operational charging performance. Furthermore, the impacts of three different shift schedule types, the increase in vehicle battery size and the coordinated charging are investigated. It turns out that more energy demanding shift schedules result in higher average charging cost per charged amount of energy. The introduction of a larger battery size as well as coordinated charging show potential for decreasing overall costs.

The dynamic origin-destination (OD) information is crucial for traffic operations and control. This paper presents a dynamic traffic demand prediction framework based on an anomaly detection algorithm. The Principal Component Analysis (PCA) method is applied to extract main demand patterns which are used to detect the abnormal conditions. The proposed approach can select prediction methods (parametric or nonparametric) automatically based on the pattern detection results. Both simulation and field observed Automatic Number Plate Recognition (ANPR) data are used to verify the proposed approach where the Kalman filter model and the K-nearest neighbor model are chosen as the basic prediction methods. The results show that the prediction framework can effectively reduce the noise of a single prediction model particularly in the abnormal conditions and provide more accurate and reliable prediction results.

AbstractThe Yangtze River is the first of China and the third-longest river in the world. It is the most developed inland water transportation system in China. Chongqing, located in the upstream of the Three Gorges Project, is not only the gateway of the industrial and commercial center of China but also the most abundant water and intermodal inland hub and material distribution center in the southwest. Frequent water traffic accidents pose severe threats to the safety of human life and property and the water environment as well as bring adverse effects on social stability. Therefore, based on the ten years of statistical data of ship accidents in 2009-2018 from the Chongqing Maritime Safety Administration (MSA), this paper summarizes the characteristics of the spatiotemporal distribution of accidents through statistical analysis of the historical data. Moreover, it proposes accident prevention and supervision methods to provide decision support for maritime safety. This research is of great significance to the prevention and control of water traffic accidents in this region. Index TermsShip accidents, statistical analysis, water transportation, risk analysis, data visualization, maritime safety.

1. Problem statement and contribution E-commerce is a rapidly growing and evolving sector. The sector is however struggling with its organization of the last-mile deliveries in order that it meets the sustainability requirements, both economically and environmentally. Different delivery methods translate into different environmental impacts. Several studies have established the advantages of collection points in terms of air pollution and/or CO2 emissions in comparison to home delivery (McLeod et al., 2006; McLeod and Cherrett, 2009; Song et al., 2009; Edwards et al., 2010; Giuffrida et al., 2016; Carotenuto et al., 2018). Zhang et al. (2018) and Verlinde et al. (2019) reach similar conclusions but only given a specific time of consumer behavior, identifying consumers’ collection trips as determining factor in comparing the environmental impact. Yet, these studies all apply for urban areas. However, consumers of all places (also suburban and rural) engage in online retailing. Moreover, web-shops hardly focus on urban inhabitants solely and often take a national approach. Given that the results of available research is bound to the local/urban context, their conclusions cannot be generalized or transferred to rural or suburban areas. 2. Methodology Following our objective to address this gap, we investigated the environmental impact associated with four last mile delivery methods from the perspective of non-food retail products in Belgium. We compared deliveries originating from a dedicated distribution center to homes (direct with 3PL or via dedicated local distribution centers) and collection points (direct or via store supply) in terms transport-related external costs for CO2 emissions, air polluting emissions, accidents, noise nuisance, infrastructure and congestion and assess the differences in impact between consumers’ residences, specifically urban, urbanized and rural areas. Both logistics flows and customer movements to the collection points were considered. To do so we applied the agent-based simulation model TRABAM (Mommens, 2019). The model uses MATSim and extends the Freight Extension (Schröder et al., 2012). 3. Results The results indicate on the one hand that homedeliveries via a well-established 3PL (with high daily volume i.e. 250.000 parcels) is the most sustainable scenario for urban, urbanized and rural areas. Deliveries to collection points are in none of the considered scenarios more sustainable than homedeliveries via 3PL, this is due to the customer movements. Yet it has to be noted that the difference between homedeliveries via 3PL and deliveries to collection points via store supply is very small to almost neglectable for the urban area. The difference becomes bigger for urbanized area and is the biggest for the rural area. Organizing a proper homedelivery system is not a sustainable option for the considered case. On the other hand, significant differences (between 10 and 15%) in terms of sustainability were found within scenario’s between urban, urbanized and rural areas. Both indicate that the environment (urban, urbanized, rural) should be considered as a parameter.

In this paper we present a methodology to more accurately calculate emissions in regional freight transportation models by taking the loading rate of vehicles into account in the emission factors. This is done by incorporating the shipment-based demand data from a multi-agent simulation framework into the route assignment. In a case study we show that disregarding the vehicle loading rate can lead to underestimations of the amount of pollutant gasses. The application of this methodology in combination with a multi-agent simulation model allows to simulate impacts of changes in logistic behavior, such as vehicle type use or the scheduling of round tours, on emissions more accurately.

The paper presents a model to estimate the demand of parcels by disaggregating nation-wide commodity flows. Secondly, the model generates parcel delivery tours to quantify transport-related effects. This model is applied to simulate parcel deliveries using cargo bikes. It consists of a two-step process: first, micro depots located close to the demand are fed with vans; second, parcels from micro depots to the customers are distributed by cargo bikes. The model simulates different shares of cargo bikes vs. motorized vans to deliver the same demand. We also studied the effect of micro depot densities and different parcel demand intensities in the same catchment area. Lastly, we compared the cargo bike tours at locations with different demand densities (parcels/km2). The results find beneficial effects of cargo bikes when the demand density and the share of cargo bikes is high. Under these conditions, the total vehicle-kilometer traveled and motorized vehicle emissions can be reduced.

Shared e-mobility is a category of emerging mobility services that includes electric carsharing, e-bike sharing and e-scooter sharing. These services are expected to reduce the negative externalities of road transport that is currently dominated by fossil-fuel-powered private car trips. In order to better inform the development and promotion of these services and indicate directions for further research, we conduct a comprehensive review of existing literature on the three shared e-mobility modes focusing on their usage pattern, demand estimation and potential impacts. We found that despite the different vehicle capabilities, all three shared e-mobility services are mainly used for short trips, and their current users are mostly male, middle-aged people with relatively high income and education. The demands of all shared e-mobility modes share many common predictors: they appeal to people with similar socio-demographic characteristics and generate higher demand in locations with better transport connection and more point of interests. Shared e-mobility services can potentially lead to positive impacts on transportation and the environment, such as reducing car use, car ownership, and greenhouse gas emissions. However, the magnitude of these benefits depends on the specific operating conditions of the services such as the fuel type and lifetime of shared vehicles. The impact of each shared e-mobility mode is also expected to be affected by other coexisting shared e-mobility modes due to both complementarity and competition. Future directions should include studying the competition between and integration of multiple shared e-mobility modes and the effect of automation.

In this work, we develop different scenarios regarding the initiatives and incentives to foster the use of electric vehicles (EVs) and test the different results on an optimal allocation tool for charging stations (ETCharger [1]) in The Hague city, the Netherlands. This is a sub-task from project ELECTRIC TRAVELLING [2]. Firstly, we overview the initiatives and incentives that have been implemented to foster the use of EVs in different regions in Europe [3] and analyzes the incentives that will be used in the implementation of the project to foster electromobility (e-mobility) in smart cities. Then the database of the city The Hague including existing transport network, and transport infrastructure parameters, especially the ones related to e-mobility etc., are introduced in order to apply the proposed tool to the case study. Based on the analysis focused on the incentives, we develop different scenarios of the transport system and use them to allocate charging stations. A comparison is done between different scenarios to select the optimal station locations according to the main goal of the project: foster increase the number of travellers by using EVs.

Air pollution and in particular PM2.5 emissions are a major problem worldwide. Road transport is a significant contributor to PM2.5 emissions in urban areas and as such it is important to understand and be able to accurately model the effects of vehicles on PM2.5 emissions. In this paper a computer vision algorithm is introduced which is able to extract vehicle trajectories from video footage. The algorithm has a 100% accuracy for overall total vehicle counting. Comparing the speeds predicted by the computer vision script to manually following a single vehicle feature on the video file, the average relative speed accuracy is 2.7% at a 1 Hz time resolution. Using these vehicle trajectories in an instantaneous vehicle emissions model and also as input to COPERT v5, tailpipe PM2.5 emissions were estimated and compared to on-road measurements. It was shown that a local sensor is not sufficient to determine vehicle tailpipe emissions due to the influence of meteorological conditions and other emission sources. Combining computer vision with an instantaneous vehicle emissions model is a useful method to evaluate changes in emissions caused by transport policies.

Many alternative approaches to achieve improvement in transportation system sustainability are continuously being proposed. The sheer number and diversity of suggested approaches available in the literature is overwhelming. Decision makers, consequently, need support in the fair and comprehensive evaluation, comparison, and combination of these proposed initiatives. A methodology for the generation of scientific-based decision support on the development of truly integrated strategies to improve transport sustainability is presented in this paper. The key contribution of the approach produced is that all the underlying complexities in the formulation of such strategies are adequately addressed and incorporated in its design. This is achieved through the development of a customized multi-objective metaheuristic simulation optimization model, called the Transport Energy Management Tool (TEMT). A case study application of the TEMT is used to demonstrate its success and usefulness and serves as a proof of concept for adoption of this approach in sustainable transport management.

Although the electrification of transportation can bring long-term sustainability, increasing penetration of Electric Vehicles (EVs) may cause more congestion. Inappropriate deployment of charging stations not only hinders the EVs adoption but also increases the total system costs. This paper attempts to identify the optimal locations for fast-charging stations in the urban network considering heterogeneous vehicles with respect to the traffic congestion at different levels of EVs' penetration. A bi-level optimization framework is proposed to solve this problem in which the upper level aims to locate charging stations by minimizing the total travel time and the infrastructure costs. On the other hand, the lower level captures re-routing behaviors of travelers with their driving ranges. Finally, numerical study is performed to demonstrate the fast convergence of the proposed framework.

Every year, some 4.5 million containers are being discharged in ports around the Netherlands. Most containers arrive on seagoing vessels in the Ports of Rotterdam and Amsterdam, from where they continue their journey on inland vessels, trains and heavy goods vehicles (HGVs) towards their destination, either within the Netherlands or elsewhere in Europe. To date, there is no complete overview of all this container transport. Statistics Netherlands (CBS) is studying possible ways of mapping the container routes within the country from beginning to end in a pilot study commissioned by Rijkswaterstaat (Department of Waterways and Public Works in the Netherlands, tr.) Essential elements in the process are new data from transport companies and customs data. Incomplete data CBS keeps track of both goods flows and transport flows through the Netherlands. The statistics on freight transport are broken down by mode: maritime and inland navigation, railway and road transport. For certain types of goods such as bulk commodities – e.g. coal and ore – this yields a fairly accurate picture. Bulk commodities tend to be carried by one single mode of transport (unimodal). However, the breakdown is less suited for other types of cargo such as containers: ‘Containers are often transhipped, moving from one mode to another,’ explains Mathijs Jacobs, traffic and transport researcher at CBS. ‘When you look at container transport broken down by mode, the data on transhipment movements is limited and there is a risk of double counting goods in the overall picture. Source data for the freight transport statistics include registers and surveys based on sampling, but these are incomplete. We fill the information gaps with estimation models. These are based on assumptions and they don’t cover the entire chain of freight transport. Especially data on individual container loads and the routes they travel are lacking.’

Analyzing and visualizing traffic data in order to better understand congestion trends, safety concerns, goods movement and capacity needs is a pressing need. Broadly speaking, there is a large amount of traffic data available today, including volume, lane occupancy, speed, and travel time, which can be used to manage transportation networks, provide traveler information and produce performance measures. This broadly disseminated data almost always treats all vehicles alike, without discriminating between trucks and passenger cars. Since trucks are critical and growing components of freeway traffic, monitoring and tracking their dynamics can reveal the impacts of freight movement on current freeway operations and over time will uncover trends useful for future planning and management. This study takes advantage of a unique data stream available for the freeway network of Portland, Oregon, USA. In addition to providing continuous vehicle count, speed and lane occupancies at 20-second intervals at more than 500 stations (1,300 individual detectors), the Portland system reports volume bins at 4 length-based classifications (< 20, 20-35, 36-30, >60 ft). Given that most vehicle classification studies are done over very short time intervals at an extremely limited number of locations, this nonstop data stream enables unprecedented insight into where and when trucks are traveling on Portlands freeways and a wealth of opportunities for performance measurement and diagnosis of their impacts. The objective of this paper is to exploit this new data stream and explore new visualization techniques that depict truck volume, truck percentage and volume-weighted average vehicle speeds along Portlands Interstate 5 corridor, an important north-south freight route between the Canada and

Autonomous vehicles present opportunities for highly integrated multi-modal urban mobility services. This study reports on the evaluation of autonomous services that can be integrated with the existing public transport network. The autonomous services considered are first/last mile feeder services, on-demand point-to-point services, robo-taxis, and autonomous car-sharing and Bus Rapid Transit. In this evaluation, the views of users, public transport operators, public transport authorities and mobility service providers are taken into account. An in-depth understanding of the objectives for each stakeholder group is needed in order to assess the impact of new mobility services. Representatives from each stakeholder group were consulted to evaluate autonomous vehicle scenarios. Using the multi-actor multi-criteria analysis method, stakeholder criteria were weighted and used to calculate overall performance scores per scenario. The results indicate that users are positive towards all autonomous scenarios. For public transport operators all scenarios, except car-sharing, perform well. Public transport authorities believe more strongly in the benefits of on-demand point-to-point services, first/last mile feeders and bus rapid transport. Mobility service providers value flexible services most. These insights can be applied to evaluate the business models of public transport operators and mobility service providers and used to shape urban transport policies.

Acceptance is often claimed to be the crucial factor for autonomous vehicle success. In this study, we examine the factors that influence the acceptance of autonomous transit feeders using a hybrid choice model. Our research is based on data from a Germany-wide online survey with more than 1,000 respondents. The influence of gender, travel behavior, individual availability of mobility tools, such as car ownership or transit pass, and previous knowledge coincides with findings from previous studies. Further, we are able to explain a large part of the unexplained heterogeneity compared to a base ordered probit model with the latent variables simplification through autonomous minibuses and pro-transit-attitude. This indicates the relevance of considering attitudes in future research on the acceptance of autonomous vehicles in order to arrive at correct interpretation and to reduce the heterogeneity in predicting models.

A successful implementation of autonomous vehicles will depend to a great extent on the acceptance of users. The individual attitudes regarding AVs are crucial in the adoption of this technology. Numerous autonomous shuttle projects have been deployed worldwide to gather insights on the technical behavior of these shuttles. Certain projects have a specific focus on user experience and acceptance of autonomous technology. As mobility patterns vary across geographic regions, the current paper investigates the user acceptance of an autonomous shuttle in the Brussels capital region with 220 respondents, 145 of which are shuttle passengers, 75 are other road users. The survey includes questions and recommendations from previous experience studies as well as relevant items from the Unified Theory of Acceptance and Use of Technology (UTAUT2). Aside from the vehicle passengers, also other road users in the proximity of the shuttle were invited to fill out a survey. The results from the study provide interesting insights for both passengers and other road users. Both groups are overall optimistic and positive towards AVs. Furthermore, adding an autonomous shuttle to traffic does not significantly influence the feeling of safety. Lastly, men are found to be more positive towards AVs than women.

A wide range of new on-demand mobility options are appearing in urban areas, one of them being pooled on-demand services (shared taxi-like services such as UberPOOL, LyftLine, OlaShare or ViaVan). Simulation studies have shown that, due to their collective nature, pooled on-demand services can bring large mobility benefits to urban areas, helping reduce congestion, pollution and space problems (ITF, 2017, 2016). These services provide flexibility to their users and trip matching only entails little travel time increases (Tachet et al., 2017). Despite these promising research findings, actual user adoption in operating pooled on-demand services is still very limited. Therefore, it is paramount to understand the demand aspects regarding pooled on-demand services in order to unleash all the potential benefits that their usage can bring to urban areas. Individual studies provide new insights into certain aspects of these services, yet they often lack a comprehensive perspective. A comprehensive perspective is, however, necessary in order to draw conclusions on how future mobility will develop. This contribution discusses the results of a series of recent scientific studies regarding the demand for pooled on-demand services in Dutch urban settings. It links the main individual research findings, and, additionally, it discusses them from a practitioners’ perspective. Other than aiming to provide a holistic picture regarding the demand for pooled on-demand services, this contribution aims at steering further joint discussion among the different transport stakeholder.

When traffic density reaches a critical point, even minor speed disturbances may trigger traffic breakdown at freeway bottlenecks. To prevent it and maintain a free flow, it is important to inform drivers about the actual traffic state correctly so as to enable them to adjust their driving behavior according to dynamic changes in the traffic state. Conventionally, a traffic state is numerically represented by such indices as density, volume, and speed, which are converted into a simple sign to provide traffic state information to drivers. However, it can represent limited information insufficient for drivers to understand dynamics of traffic flow. In the present paper, we propose a novel method to represent complex traffic dynamics intuitively by applying data sonification, a technique for rendering sound in response to the online data. Concretely, individual vehicle data collected by loop detectors at a freeway bottleneck is rendered in sound signals. As a result of the conducted sensory evaluation experiment, it was found that respondents could correctly distinguish the traffic states including the free flow, flow at crowded condition, flow just before traffic breakdown, and jam flow.

The technological advances in connected and automated vehicles (CAVs) enable cooperative (automated) vehicles to exchange information with not only infrastructure but also among vehicles. Joint control of traffic signals and vehicle trajectories has the potential to improve traffic operations and environmental economy on urban roads. Exiting literature on CAV platooning on urban roads mainly focus on driver assistant systems, cooperative vehicle intersection control algorithms, CAV trajectory optimization, and the integrated optimization of traffic signals and vehicle trajectories. However, most of these algorithms were designed to optimize simple objective functions of the platoon leaders, which cannot reflect the benefits of the whole platoon. We propose a hierarchical approach for joint design of signal timing and cooperative (automated) vehicle trajectories at typical four-arm intersections with predetermined phase sequence. The upper layer of the proposed control approach determines the optimal lengths of signal phases based on optimal control and a surrogate model of CAV platoon dynamics. For simplification, only the accelerations of the platoon leaders and the first-stopping vehicles are optimized, while the other following vehicles are represented using a car-following model. The running cost considering multiple terms (i.e. throughput, comfort, travel delay, safety, fuel consumption) is piecewise according to the signal indication and the vehicle sequence in the platoon. Additional penalty terms for first-stopping vehicles have an advantage on longer horizon like consecutive signal cycles, rather than limited within one signal cycle. The acceleration and speed are constrained within maximal and minimal bounds. The lower layer optimizes trajectories of all vehicles using model predictive control approach under the fixed but optimal signal plan resulted from the upper layer. The signal indication in the lower layer is transferred to the first-stopping vehicle at the beginning of the current signal cycle, which can operate the stopping vehicles smoother than trajectories without communicating the signal information.

Transit signal priority (TSP) is a common strategy to improve bus right-of-way at signalized intersections. However, TSP systems have several challenges, such as negative externalities for non-transit users, and handling conflicting priority requests. Considering recent advances in connected vehicle technology, we propose a user-based signal priority strategy (UST) to facilitate bus movement at intersections while minimizing adverse effects to non-transit users. Additionally, we extend UST by minimizing bus scheduled delay (UST-SD) to compensate bus delay that is caused by network congestion. We compare UST and UST-SD with a conventional TSP ring barrier controller (RBC) at an isolated signalized intersection in a microscopic simulation environment. The findings show that the proposed strategy improves user and vehicle performance measures while providing priority for buses.

The development of data-driven smart arterial systems that enables reduction in delays and increases the travel time reliability is a valuable tool for improving of arterial performance. Understanding the synergies and differences between speed data sets and traffic signal controller data is necessary for the efficient deployment of Intelligent Transportation Systems (ITS). It is not always feasible to fully instrument an intersection that provides data on optimal performance metrics. Therefore, it is necessary to establish a baseline performance metric for a given corridor to develop an ITS management plan. To that effect, this study conflated crowd-sourced anonymous probe vehicle data on vehicles trajectories and speeds with high-resolution traffic signal data sets. Interdependencies between the two datasets were examined and baseline corridor performance metrics were established. The analysis included an evaluation of the data sets for a 7.3-mile corridor in Burlington County, New Jersey (AADT ranging from 10,000 45,000). A GPS-equipped test vehicle was used to establish reliability of probe-vehicle data, which was then compared to near-term performance measures derived from high-resolution traffic signal data. Based on the analysis of approximately 1.7-million probe vehicle data points using visualization tools, the study demonstrated that the integration of multiple data sets provides a viable mechanism for the development of reliable, visually intuitive, arterial performance metrics. The study results also indicate that long term speed data from anonymous probe vehicle data could be used to evaluate traffic signal data to measure arterial performance measurement.

First-Mile/Last-Mile is one of the burdened urban transportation problems limiting the effectiveness of public transit. Recently, the new emerged shared micro-mobility, e-scooter, came to fill this network gap and gained its popularity across the world. For this specific short-range trip mode, the demand is highly volatile from time to time, so demand and volatility prediction should be incorporated. For this reason, Box-Cox transformation, seasonal ARIMA (SARIMA), and family of GARCH models were used to predict its hourly demand and volatility, using hourly e-scooter demand from 23 Jan to 30 Apr 2019 in Thammasat University, Thailand. The combination of these models provides a very important understanding of the demand pattern that is necessary for operational planning.

Free floating shared electric vehicles have the potential to contribute to a zero-emission urban transport system. In comparison to station based car sharing (one or two-way), the vehicle can be left anywhere within a given area instead of at designated stations. This gives the user more flexibility in driving the car. Downside of free floating car sharing with electric vehicles is that charging stations are needed across the operated area without certainty of where demand is. A lack of charging stations has been the most important reasons for shutting down services [1]. If charging infrastructure is available, car sharing operators can use different strategies for charging their fleet. These include (1) charging the cars as operators, (2) allowing users to charge and (3) incentivizing users to charge. This research is the first to empirically analyse different charging strategies for electric free floating shared vehicles.

The degree of electrification of vehicle powertrains rose significantly in the last years. For fully electric vehicles particularly longer trips are a problem because of limited range, the necessity to charge and consequently higher travel time. Thus, accurate data on energy consumption, driving and charging options and corresponding routing are critical for long trips. This requires innovations that foster the popularity of battery electric vehicles (BEVs). A combined strategy including route, charging and velocity suggestions can enable seamless use of electric vehicles on long distance trips. Based on a mixed-integer nonlinear program (MINLP) a heuristic approach is shown that calculates route and charger options in heterogeneous networks followed by charging amount and prospective velocities in a time-optimal way. For the comparison of the obtained route-charging-velocity plan, a second dynamic programming (DP) approach is shown. A realistic driving scenario serves for method evaluations. The optimal strategy's aim is a minimum travel time, assuming a predefined final state of charge (SOC). The discussion evaluates both approaches concerning computing time and the obtained results, particularly velocity and charging time. The methods' real-time applicability is shown by calculating optimal strategies.

This paper presents a new agent-based model able to simulate innovative flexible demand responsive transport services, specifically thought to solve the last-mile problem of mass rapid transit. This is particularly needed in areas characterized by insufficient transit supply and lower sprawled demand, where new technologies have the potential to dynamically couple demand with supply. The model compares the performances of two feeder services, one with flexible routes and stops activated by the requests of users, and the other with fixed routes and stops, satisfying the same demand. The case study city is Catania (Italy), where such services could increase the ridership and coverage of a 9 km long metro line that connects the city centre to peripheral areas. Different scenarios have been analysed by comparing a set of key performance indicators based on service coverage and ridership. The first results highlight the validity of the model to identify optimal operation ranges of flexible on-demand services and pave the way for further investigation needed to understand their acceptability and economic viability.

The operation of charging infrastructure is often not profitable yet. However, the integration of charging infrastructure in microgrids enables the introduction of innovative business models, e. g. by using PV generation and storage units. This study presents several business models, i. a. based on self-supply of electricity and smart charging algorithms. These are evaluated by means of real-world transaction data of the EUREF campus microgrid in Berlin, Germany. The dataset comprises data of 3346 transactions of foremost commercial fleet vehicles from the year 2018. The microgrid configurations evaluated in this study involve the integration of PV generation, battery storage, individually as well as combined. A reference is provided by a comparison to the conventional operation of charging infrastructure. The evaluation shows that the integration of PV generation in combination with smart charging is profitable, due to lower expenditures by local energy supply utilization. Additional integration of a stationary battery is less profitable. However, it increases autarky and flexibility by charge during evening and night hours.

Last mile delivery is seen as the most expensive part of the supply chain, and causes significant external effects (e.g. pollution, land use, noise). Therefore, much research has been devoted to finding alternative vehicles to deliver parcels such as drones and cargo bicycles and to finding alternative delivery locations such as parcel lockers. Parcel lockers are seen in most simulation studies as the most sustainable option. However, most of these studies ignore that customers might drive a car to pick up parcels. Also, parcel lockers require space 24/7 whereas a delivery van only requires a parking space for a few minutes while the parcel is handed over. However, during home delivery more space is required for the delivery van while driving due to the increased vehicle kilometer traveled compared with delivery to parcel lockers. The contribution of the study reported in this paper is a simulation tool to evaluate different parcel delivery strategies and customer movements based on real parcel delivery trip data and statistics about the parcel receiving habits. Secondly, the study uses the resulting data to compare the Time-Area requirements of home delivery and parcel lockers.

The present traffic safety measures appealed to the eyes,then they arent fully effective in some cases, like for drivers not looking ahead, hazy drivers and so on. On the other hand, drivers can unconsciously hear and can easily understand the meaning by the voice warning and the information provision. Then the auditory system can become the effective traffic safety measures because it can directly informs drivers. So, we developed the system which transmitted voice warning and voice information directly to all drivers at open section. And we installed this system in some expressway sites.

In this paper, a concept of an IoT based solution for a Bike Sharing Platform is presented. The developed platform implements a new layer of instrumentation that allows data collection that could be used for, for instance, route suggesting algorithms, fleet management and distribution, traffic analysis, crashes detection, crashes reconstruction, and driver profiling. All these possibilities are of major interest in today’s socioenvironmental context. The use of bicycles and sharing platforms should be promoted and encouraged, but for that to happen, some limitations regarding safety and insufficient user engagement must be overcome. The work developed under this paper aims to mitigate those barriers and provide a functional, innovative, and efficient solution.

Shared autonomous vehicles (SAV) are expected to become part of mobility on-demand systems in the near future. The vehicles’ ability to move autonomously has the potential to change the way transportation is perceived. For instance, a system with autonomous vehicles can be used by persons without a drivers’ license and the fact of not needing staff to drive or relocate vehicles enables services to expand into less dense areas. In an interurban context SAVs can contribute to increase the availability of public transport. This service that nowadays is performed mainly by bus, with the payment of drivers’ wage and the need of diverting from the direct path to cover more demand, could be performed by smaller vehicles using faster routes making it more attractive to the daily commuter.

Urban mobility is under pressure, resulting from the still ongoing urbanization, urban densification, urban expansion, car-dominated cities and increasing mobility demand. In order to protect the accessibility, livability, safety, sustainability and efficiency of the cities of the future, transport policies focus on increasing the utilization rate of public transport and shared mobility as these are sustainable modes of transport in urban areas. This research is focused on an application of a demand-responsive autonomous one-way carsharing service as a first- and last mile solution to increase the attractiveness of public transport, currently suffering from last-mile connectivity problems. Multiple researches are published which aim at modeling this Autonomous Mobility on-Demand (AMoD) systems for certain case studies in order to assess the impact of AMoD systems on urban mobility. The far most popular transport modeling paradigm in doing this is agent-based modeling (ABM), in which the urban mobility system performance results from the interaction of agents according to their individual behavior. However, there are certain shortcomings in research on AMoD systems. Firstly, these researches primarily focus on the supply-side of AMoD systems, often assuming certain extreme demand situations like a replacement of all taxi demand for AMoD demand. An alternative for assuming such demand situations is predicting the demand using existing travel demand estimation models. Secondly, the impact of operational variables on the financial viability of AMoD systems is not yet clear, while the financial viability plays an essential role in the implementation of automated vehicle applications. Therefore, this research aims to predict the financial viability of AMoD systems based on the main costs and revenues for various operational scenarios of AMoD systems. The objective of this research is to answer the main research question: What is the financial viability of Autonomous Mobility on-Demand operations as a first- and last-mile solution for public transport in urban areas?

The introduction of a carsharing service reduces both private car ownership and total vehicle miles traveled. Due to the environmentally friendly modal shifts, policy makers are keen to increase market share of carsharing. While past research has focused on both the users of carsharing services and their general mode choice, little is known about access trips to vehicles of station-based carsharing services. However, because access to a mode is critical for its success, gaining insight into the way people experience access trips is an important step towards increased mode acceptance. In this paper we present model results based on a stated choice survey regarding access trips to carsharing stations conducted by users of a regional carsharing provider in Germany. After giving a brief overview of the conducted survey and the results of a descriptive analysis, we present the results of multiple multinomial and mixed multinomial logit models. Results of the multinomial logit models show that trip-related variables are the most important determinants for access mode choice, while only a few socio-demographic parameters are significant. We estimated mixed multinomial logit models to find out how consistent respondents answered across the choice situations. The results show that respondents were not always consistent across their choices and would, e.g. choose public transportation even if that entailed long distance travel or waiting times. Our findings are consistent with both research regarding public transportation access and the small pool of research regarding access of carsharing vehicles.

The promise of Mobility-as-a-Service (MaaS) is that it decreases the need to own a car and contributes to a more sustainable transport system. However, MaaS also offers relatively easy access to car-based travel alternatives which may result in substituting public transport trips by car trips. An important question therefore is: which type of traveler is going to adopt MaaS and which impact is this going to have on their mode choices? This paper explores this question by presenting the results of a stated choice experiment conducted in the Netherlands. Travelers are presented with MaaS bundles that vary in accessibility to transport services and price and they respond to a range of questions about bundle adoption, change in transport mode, and willingness to shed one or more cars. The results suggest that if MaaS bundles are given for free to the travelers, this has the potential to change their frequency of mode use. For example, if the MaaS bundel includes unlimited bus, tram and metro (BTM), even travelers who solely use car will then use BTM more. However, realizing this potential is not very likely, because when travelers have to pay for MaaS, adoption rates are rather low, in particular of those who use car the most. In addition, the willingness of car owners to shed their cars is very low, suggesting that currently MaaS is not conceived as a viable alternative for car-ownership. On the other hand, current public travelers seem most interested in MaaS and results indeed as expected suggest that this increases their car use. Overall, the trends reported in this paper adds to a growing insight that MaaS contribution to sustainability may be smaller than generally believed.

The continuous increase of urban population and trend towards urban sprawl in European cities have introduced a change in mobility patterns. In the digital age, real-time information travel planners become a key enabler of travel behaviour change, and can be applied for encouraging more sustainable habits. This paper explores the motivational drivers underlying the adoption and use of these Smart Mobility solutions. Since end-users expectations are particularly important for achieving the most successful design of travel planning applications, an ad-hoc travellers survey is designed and carried out in the metropolitan area of Madrid (Spain). The assessment methodology used by the research Principal Component Analysis is proposed as a replicable step-by-step procedure. The innovative contribution of this research is the consideration of three different categories of travellers according to the origin and destination points of their most frequent trips. Our results highlight the different needs of each group of travellers, but also point out their common motivations: the need of user-friendly devices, the need for control and their environmental awareness. In contrast to previous literature, this study does not identify privacy and security concerns as significant latent constructs.

Max pressure (MP) is a distributed strategy for adaptive urban traffic signal control. Real-time queue estimation for road links is indispensable for MP-based traffic control. All works conducted so far on MP traffic signal control assumed that accurate information of vehicle queues was directly available in real time. This paper studies joint queue estimation and MP control for signalized urban networks with connected vehicles. For the sake of practical significance, the cases of link queue estimation and lane-wise queue estimation were both considered as input to the MP traffic signal control. A congested 3*3 network was emulated using AIMSUN to evaluate the performance of the developed queue estimation and MP traffic signal control algorithms, with study results reported.

The development of new more efficient freight transportation services requires in depth understanding of the engaged stakeholders behavior. Simulation Games (SG) have been used to study decisions and raise awareness over complex transport problems. This paper investigates the possibility of applying a simulation game as an innovative data collection tool for choices related to freight transport. Games sessions were organised with Dutch logistics managers and data on their choices for synchromodal services were collected. Model results revealed reliability, costs and reduced work load as key factors making shippers to opt for more synchromodal services. Keywords: synchromodality, simulation games, behavioural data collection, service choice, multinomial choice model.

Agent-based freight models are used to simulate a plethora of agents, agent attributes, and operational environments. The population in these models typically comprises business establishments or firms. Due to lack of data, attributes are often limited to readily available traits such as firm size and industry category. This research addresses the data gap issue by building a data development engine (DDE). The DDE extracts key company data from big, online, text-based information systems and builds a dataset of companies to use in model estimation. The primary, initial focus of the DDE is to develop data regarding strategies that are adopted by companies and used to guide company decisions. Earlier work has shown that including firm strategies in an agent-based freight model is important for estimating freight agent behavior and, ultimately, impacts on energy use and vehicle-miles traveled. Factor Analysis and Structural Equation models are used to identify firm emphasis areas and a latent variable that weighs the relative importance of two emphasis areas (service vs. product innovations). The resulting strategic data is used to inform a model of private fleet ownership.

Truck surveys are carried out continuously in all EU and EEA countries according to Eurostat statistics regulations. These surveys are known to have a high reporting burden, and a major challenge is that a large share of trucks is incorrectly reported to be out of order during the reporting week, resulting in low quality data. This paper highlights how smart data capture can help reduce reporting burden and increase data quality. It further provides perspective on requirements to other data sources with potential relevance for smart and improved transport statistics, as well as their current limitations.

Autonomous vehicles (AVs) will impact roadway safety, congestion, mode splits, trip distances, and air quality. We estimate the net social benefits of each AV to be nearly $4,000 per year, thanks to crash savings, travel time reductions, and parking benefits. This seminar will examine the design & results of agent-based models for shared AV (SAV) operations. Results suggest that SAV costs and prices will be relatively low (e.g., under $1 per mile, especially when shared en route), with empty-vehicle travel on the order of 10 to 20 percent of fleet VMT. If SAVs are smaller and/or electric, and dynamic ride-sharing (DRS) is enabled and regularly used, emissions and energy demand may fall. If road tolls are thoughtfully applied, using GPS across all congested segments and times of day, total VKT may not rise: instead, travel times - and their unreliability - may fall. If credit-based congestion pricing is used, traveler welfare may rise and transportation systems may ultimately operate near-optimally. This presentation will present research relating to all these topics, to help engineers and their communities to strategically ensure a better future.

Metropolitan authorities continue to seek programs and initiatives to reduce emissions in their jurisdictions. It has been shown that transitioning from fossil fuel to electric propulsion of transportation can help realize this goal. However, the current market penetration of electric vehicles (EVs) compared to internal combustion engine vehicles (ICEVs) remains very small. This paper proposes a framework to address this problem over a long-term analysis period. The paper accounts for consumers vehicle-purchasing propensities and their route choices, locations of EV-charging and ICEV-refueling stations. In the proposed framework, new EV charging stations are provided at selected locations and/or existing gas stations are repurposed by the transport agencys decision maker (through policy) in conjunction with the private sector (through investment). The paper presents a bi-level mathematical model to capture the decision-making processes of the transport agency and the travelers. Underlying the framework is a solid theoretical foundation for the EV charging network design. The design problem is solved using an active-set algorithm. The study results can serve as guidance for metropolitan transport agencies to establish specific locations and capacities for EV stations and thereby to contribute to long-term reduction of emissions.

Most V2X applications/services are supported by the continuous exchange of broadcast messages. One of the main challenges is to increase the reliability of broadcast transmissions that lack of mechanisms to assure the correct delivery of the messages. To address this issue, one option is the use of acknowledgments. However, this option has scalability issues when applied to broadcast transmissions because multiple vehicles can transmit acknowledgments simultaneously. To control scalability while addressing reliability of broadcast messages, this paper proposes and evaluates a context-based broadcast acknowledgement mechanism where the transmitting vehicles selectively request the acknowledgment of specific/critical broadcast messages, and performs retransmissions if they are not correctly received. In addition, the V2X applications/services identify the situations/conditions that trigger the execution of the broadcast acknowledgment mechanism, and the receiver(s) that should acknowledge the broadcast messages. The paper evaluates the performance of the context-based broadcast acknowledgment mechanism for a Collective Perception Service. The obtained results show the proposed mechanism can contribute to improve the awareness of crossing pedestrians at intersections by increasing the reliability in the exchange of CPM messages between vehicles approaching the intersection. This solution is being discussed under IEEE 802.11bd, and thus can be relevant for the standardization process.

Advanced driving assistance and automation technologies promise to disrupt road transportation as it is known today. One of the first example of widely available Advanced Driver Assistance Systems (ADAS) is the Adaptive Cruise Control (ACC), which regulates the longitudinal vehicle dynamics requiring only the supervision of the driver. Unfortunately, the actual properties of these systems still remain almost unknown to the research community. Experimental campaigns involving these vehicles are indeed still limited by the resources they require and by the difficulties related to their setup. The available ones, although very important to shed light on the main features of commercial ACC systems, are usually limited in number of vehicles, traffic dynamics and geographical coverage they involve (see for example Milanes and Shaldover, 2014, Knoop et al. ,2019, Makridis et al., 2019). To provide the research community with a new tool to study the behavior of different ACC systems, the present study presents the data collected during a series of car-following experimental campaigns involving 12 commercial ACC equipped vehicle models of different brands tested in different conditions. The trajectory datasets have a sampling rate of 10Hz. Post-processing has been performed in order to filter or remove problematic sections such as tunnels, roundabouts and tolls. First experimental results confirm the previous findings about reaction time, time headway and string stability and provide inputs to researchers and policymakers on the potential impact of ACC-equipped vehicles on traffic dynamics when the penetration of these vehicles will increase in the near future. Additionally, they suggest several directions to consider in order to improve the performance of the ACC controllers, reduce potential downsides arising from ACC heterogeneity on public roads and maximize the benefits that such systems can bring to traffic flows. The database of vehicle trajectories used in this study is publicly available to the community to facilitate further research on the topic.

Recent research has demonstrated the potential benefits of connected, autonomous vehicles (CAVs) to the performance of urban networks. Specifically, several proposals have been made for policies and related technologies that either perform more efficiently when the proportion of CAVs is relatively high or that exclude human driven vehicles (HDVs) altogether. This same body of research has also identified several challenges faced by such networks, especially in the context of shared autonomous vehicles (SAVs). We propose a lane-use policy for networks of exclusively CAVs with the goal of preserving priority within any two-class, arbitrary priority assignment regime. We investigate the merits of such a policy by adopting a simple occupancy-based, two-class priority scheme in a network of SAVs. We will demonstrate that by granting and preserving priority for occupied vehicles, average travel times and speeds for passengers are improved with limited degradation in these measures for other, i.e. unoccupied, vehicles. The proposed lane-use policy is developed on realistic physical limitations of the street network and without the need for trajectory reservations.

Traffic flow effects of cooperative adaptive cruise control (CACC) have not been fully understood, though such understanding is essential for the forthcoming research and applications concerning connected and automated vehicles as well as the implication for future traffic management and control. This paper aims to explore the impacts of CACC on traffic flow efficiency based on in-depth microscopic simulation studies using an enhanced version of AIMSUN. First, the Gipps car-following and lane-changing models used by default in AIMSUN were replaced or enhanced with more advanced car-following and lane-changing models to improve the capability of the simulation system in realistically reproducing capacity drop phenomena and capturing traffic merging processes. Second, a simulation platform was established using the enhanced AIMSUN with respect to a 10-km Dutch freeway stretch that includes multiple bottlenecks involving recurrent congestion. Third, we explored on this simulation platform the impacts of CACC on traffic flow mixed with manually driven vehicles and CACC equipped vehicles, in consideration of a variety of CACC market penetration rates. The study results suggest that the CACC impacts are quite positive.

This article presents the global optimization routing system implemented in LOGISTAR . LOGISTAR is an H2020 project funded by the European Commission which seeks for the effective planning and optimization of transport operations in the supply chain by taking advantage of horizontal collaboration. During the development of the system, several particular goals were planned, one of them being the review and update of the state of the art about models and techniques for the optimization of the freight transport network for horizontal collaboration, as well as the optimization of transhipment planning and scheduling in hubs. The reason for the popularity and the importance of such a domain is two-fold: the social and economical interest they generate, and their inherent scientific interest. On the one hand, vehicle route optimization solutions designed to deal with real-world situations related to transport or logistics entail profits for logistics companies. Nevertheless, most of the problems arising in this field have a high computational complexity that algorithms must deal with, making the resolution of such problems a major challenge for the scientific community, and usually, a testbed for the design of new methods in the field of combinatorial optimization.

Accurately mining the spatiotemporal distribution characteristics of demand for taxi travel is helpful to better dispatch and guide the distribution of taxi supply, so as to alleviate the imbalance of taxi supply and demand in high passenger demand areas. Based on the multi-source traffic data including taxi GPS data, taximeter data, public transport transactions data and Point of Interesting (POI ) data in Beijing, correlation analysis methods were used to select the influencing factors of taxi travel demand, and establish a multi-dimensional set of influencing factors. A Geographical Weighted Regression (GWR) based influence model of regional taxi travel demand is established, and 1398 regions in Beijing is taken as an example to quantitatively explore the impact of various factors on taxi demand under different space-time conditions. The results show that the density of high-grade residential area, financial and commercial land, office area and recreational area in the city periphery has an obvious positive impact on taxi travel demand; While the density of residential land in urban periphery and the office area in the city center has a negative correlation with the taxi travel demand; In addition, the impact of the public transport volume in the peak hours on the taxi travel demand in the city center and peripheral areas is significantly different.The analysis of the distribution characteristics of taxi travel demand in different space-time dimensions provides an important support for the rational allocation of taxi transportation service resources.

Truck traffic has a comparatively larger impact on safety, traffic congestion, pollution, and pavement wear than passenger vehicles. Appropriate planning and operation of truck movement is necessary to reduce these impacts. Truck movement has traditionally been measured through surveys, which are limited because they are costly and time consuming. In this study, we propose the use of large streams of GPS data to estimate truck origin-destination flows. We use a multinomial logit structure to estimate destination choice models for five time periods to show the distribution of truck trips in the province of Alberta, Canada.

Eco-routing has been proposed as a means of distributing traffic in cities to improve mobility sustainability [1-3]. The implementation of eco-routing in real-life requires a diverse set of information, including heterogeneous and legacy sensors often already present in the city infrastructure. In this work, we present a modular architecture leveraging Internet of Things (IoT) technologies that enables collecting the necessary data, fusing it, and inferring the information required for the eco-routing application. Further, we formulate the eco-routing problem as a multi-objective optimisation to distribute traffic targeting better pollutant emissions vs travel time trade-offs. A city manager chooses the desired solution, which is used to serve routes, e.g. to a fleet committed or incentivized to contribute to an environmentally friendlier city. Preliminary results show the potential impact of eco-routing using real data for a mid-sized European city, and the impact of using static emission weights in the optimization formulation.

Maintaining high operation performance and at the same time having high transporting capacity is a challenge for urban rail lines during unexpected disruption. In this paper,we focus on adjusting the train timetable in case of a complete blockage with fixed disruption length. A mixed integrated linear train rescheduling model, where short-turning, cancelling and adjustable dwell time and running time are included, is proposed to minimize total train delays and services cancellation under different dispatchers reaction time towards disruption. To evaluate the rescheduling impacts on passengers, we later establish an accurate passenger flow model considering time-dependent passenger ODs. The main objective is to minimizing passenger travel time. A case study is carried out for the disruption scenarios on Beijing Metro Line 7. It is illustrated that optimal rescheduling solution is sensitive to reaction time. Shorter reaction time mitigates train delays as well as passengers'travel time. Longer reaction time increases the numbers of affected train services during the disruption and make it more difficult recover to original timetable.

The increasing use of the private vehicles makes it increasingly fundamental to understand how to induce users to prefer public transport. In this regard, studying the quality of public transport and the methods to increase them is central. There are several factors involved in the definition of the quality level of a public transport system: reliability, regularity of the service, crowding level, on-board comfort, etc. In this paper, the research is focused on the analysis of on-board comfort, in particular, on the comparison between the level of comfort of two different public transport systems: metro and bus. The investigation of on-board comfort has been carried out based on the collection of kinematic data (accelerations along three axes), through a sensor installed inside public vehicles. The acceleration values were analysed according to the ISO 2631-1. The aim is to identify which of the two transport systems is the most comfortable and to identify strategies for improving the level of comfort.

Traffic performance has many positive and negative consequences to the environment and society. These external effects are ever more often considered in the traffic system planning and administration. Desired effects of traffic can be thought as traffic performance policies. It is also possible to support these policies through traffic management and traffic signal controllers. In this study we introduce a general framework for a process flow which allows signalised junction controllers to adapt into desired policy. Also, we present an example implementation of the processes of the framework, and experiment with it by optimising a signal controller in a microscopic traffic simulation environment.

E-grocery is a growing phenomenon that has the potential to rapidly change the way shopping is performed and goods distributed in cities. The impacts of this innovation can vary according to the delivery service performed, thus affecting positively or negatively the overall sustainability of urban freight transport. Understanding the dynamics of demand (i.e. consumers) will be useful to know how supply (i.e. supermarkets) should adapt, and how policy-makers should deal with this phenomenon. This paper presents an agent-based model to simulate different e-grocery scenarios at a neighbourhood scale. Agents are characterized with individual utility functions from a latent class model based on a stated preference survey performed in Rome (Italy). First results of a one-month simulation allow deriving some useful conclusion on the impact different grocery channels can have on travelled distance and consumer shopping time and formulating some policy implications.

Against the backdrop of the expansion and densification of metropolitan areas around the world and the recent evolutions in logistics (such as demand-driven supply chain practices), the traffic and environmental impact of urban freight movements is a significant concern. A small share of goods vehicle movements among all vehicle movements accounts for a disproportionally large share of environmental impacts in a metropolitan area (Coulombel et al., 2018). A new set of logistics solutions, including those taking advantages of technological innovations, have been proposed to mitigate the negative impacts associated with urban freight flows (Taniguchi et al., 2016) and, for fulfilling the needs of designing and evaluating them, a number of novel urban freight modeling frameworks have been proposed (Chow et al., 2010; Comi et al., 2012; De Jong et al., 2013). Such models often take a disaggregated, “agent-based” approach, partially at least, to simulate the behaviors of multiple agents that are engaged in urban freight-related activities, and interactions of thereof, for enhancing model capabilities beyond traditional aggregate models. However, urban freight simulators using such agent-based models are still limited in replicating logistics decisions at the level of detail to evaluate various types of innovative logistics solutions. Furthermore, a number of the novel modeling frameworks still remain in isolation and there is a need for their integration. Moreover, to the best of our knowledge, the integration between the agent-based freight and passenger models that considers the same agents (e.g. individuals, establishments, and drivers) consistently, is not yet available for real-world policy analysis (Schroder and Liedtke, 2017). We will present the framework of an urban freight simulator, SimMobility Freight, which is newly developed as a part of SimMobility, an agent-based urban transportation simulation platform (Adnan et al., 2016). SimMobility involves multiple interacting agents on multiple temporal dimensions and its passenger travel simulation follows an activity-based paradigm. To date, it has been implemented for two metropolitan areas in the U.S., Boston and Baltimore, and Singapore. Together with the other parts in SimMobility, SimMobility Freight aims at the integration of disaggregate, agent-based freight and passenger simulation models. The core components of SimMobility Freight predict commodity contracts, logistics planning, and goods vehicle operations and, therefore, capture the connections among land use, commodity flow, goods vehicle traffic, and transportation network conditions. Furthermore, the adaptive modular approach allows for the extensions regarding key decisions which impact urban freight; SimMobility Freight also predicts overnight parking choices and pickup/delivery parking decisions. The disaggregated nature of the models allows for measuring the impacts of the changes in various aspects in logistics operations and evaluating solutions, such as crowd-shipping, delivery consolidation, carrier cooperation, and delivery demand management with/without extension modules. SimMobility Freight is currently available for Singapore, one of the largest metropolitan areas in Asia, and has been used for evaluating a series of logistics solutions (Gopalakrishnan et al., 2019; Mepparambath, 2019).

Route planning in public transport receives an increasing interest in smart cities and particularly in metropolitan cities where crowded and jammed traffic is daily recorded in the transportation network. The availability of digital footprints, such as ticketing logs, or load on board the trains, provides a relevant opportunity to develop innovative decision-making tools for urban routing of passengers in order to assist them to better plan their journeys. In this paper, we propose to enrich existing journey planners with predictive travel information to enhance the passenger travel experience during his journey. For that purpose, we augment the planned trips with predictive passenger flow indicators such as the load on board trains, and passenger attendees at the station. These indicators are forecasted along the journey with the help of the developed machine learning models. The experiments are conducted on a real historical dataset covering the Paris Region with a focus on a railway transit network that serves mainly the suburb of Paris.

Battery electric vehicles (BEVs) are playing an increasingly important role in personal mobility due to the wish to counteract climate change and political regulations concerning carbon dioxide emissions. Nevertheless, there are obstacles that need to be overcome. Especially long-distance journeys are problematic due to long charging stops and range anxiety. It is a drivers wish to fulfill a given driving task in a time-optimal way. But in the BEV case, driving faster does not necessarily lead to a decreased total travel time. The vehicle routing and charging problem is formulated as a mixed-integer nonlinear program (MINLP) and solved using mathematical optimization methods. First, time-minimizing vehicle routing with charging stations providing different powerlevels is discussed. The program returning the exact result is significantly faster than previous ones. Afterwards, the model is extended: driving speed becomes adjustable. A combined timeminimal optimization of which route to take, how fast to drive, where and how much to recharge is the result. The combination of these four parameters has never been studied before. It is shown that up to 14.48 % of driving time can be saved in our examples by incorporating the choice of a driving speed.

The number of personal cars is expected to rise at a rapid rate, particularly in developing countries, such as China and India. These increases will hamper the global effort to reach the climate target set by the Paris Agreement as most of these vehicles will be fossil-fuel powered. Urban carsharing is a Transport Demand Management measure that can reduce and delay vehicle purchasing. However, little has been reported on its utilization, particularly in the context of developing countries. This study provides empirical results on how a dominantly station-based carsharing service in Bangkok city, Thailand, is utilized. We analyzed the data generated by the service to provide descriptive information on the users' behavior. We also clustered the users into three groups; frequent users, typical car renters, and young car sharers to provide new insights into how carsharing is utilized by the Bangkokians. The outcomes provide a reference case for future studies and support policy-making to promote carsharing in a similar context.

Despite its relatively small size, Luxembourg City is facing major challenges in terms of traffic congestion (33h/person/year spent in traffic congestion according to the INRIX 2017 Global Traffic Scorecard), primarily caused by an important share of cross-border commuters driving more than 35km on average for home-work trips, and the highest car ownership rate in the European Union (0.75 vehicles/person). With the goal of reducing the car pressure and promote sustainable mobility, the country is heavily investing on public transport (PT) and in particular on new technologies that enable smart and cleaner transport. Three trends towards next generation PT systems are observed: 1) introduction of greener vehicles such as electric/hybrid buses (e-buses), 2) focus on high service quality (e.g. increased ride comfort via mitigation of stop-and-go driving) and 3) reduction of emissions and bus operating costs related to fuel/energy consumption and equipment wear and tear. In addition, the digital revolution is offering new opportunities to empower vehicles with new Information and Communication Technologies enabling the adoption of Cooperative Intelligent Transportation Systems (C-ITS) and new ways of managing the system in a more efficient way. These trends however bring new challenges. The first challenge is posed by different operational characteristics and constraints of e-buses, e.g. they need to periodically recharge batteries at e-charging stations placed in selected stops and terminals. This brings additional complexity into PT operations since charging may have an impact on line scheduling. The second challenge, relating trends 2 and 3, is how to provide comfort- and cost-related benefits without negatively impacting general traffic performance. Relying solely on strategies such as Transit Signal Priority (TSP), which prioritizes PT vehicles at signalized intersections, might cause congestion effects that could backfire on the PT system itself. In this study we argue that we can make better use of e-buses and the C-ITS paradigm by introducing new strategies explicitly optimizing the interactions within the PT ecosystem components consisting of PT vehicles, traffic signals, and electric bus charging infrastructure. The main novelty of eCoBus is that these strategies combine cooperation and negotiation within the whole PT ecosystem, enabled by C-ITS connectivity. The main research challenges are in formulating and solving complex optimization problems to manage the fleet at the planning phase, while developing novel real-time control strategies to better manage the vehicles at the operational phase. The proposed system is tested and evaluated in large-scale simulations and in real-world controlled experiments supported by our PT industry partners—Volvo Buses and Sales-Lentz (PT operator).

Land-transportation can´t further meet its demands. Crowded highways, crowded cities, dangerous emissions, traffic accidents, delays, expensive railways. Solutions are being sought to transfer a large part of passengers- and especially freight-traffic to (high-speed) rail and to go electromobility, car-sharing, 5G-connectivity, autonomous ride, MaaS-transport-coordination, Hyperloop-type solutions. However, all these solutions have other problems and limitations. Solutions are not sought where they really exist - in the mutual adaptation of road and rail vehicles and their deep cooperation. ComplexTrans-project shows that simply adapting dimensions and functions of road and rail vehicles can eliminate (at least substantially reduce) all the problems of existing land transport, resulting in - ample parking space, - reduced traffic density in and outside of cities, - electric-vehicles with unlimited range and cheaper than standard cars, cheaper and easy affordable recharging of batteries, - pseudo autonomous ride, - transfer of intercity freight to rail, - replacing part of continental air-transport, - self-financing rail-transport, - deep cooperation with renewable energy resources sun and wind - significant reduction of energy consumption in transport, - independence of oil and a significant possibility for emissions reduction, - resistance to the transmission of communicable diseases - and many others. The main components of the ComplexTrans system are - high-speed trains (200 km/h) consisting of passenger-freight double-decker coaches and connected fast rail cargo cars - road transport modules for cargo or passengers - adapted cars with changed dimensions, design and functions and follow-up systems - passenger-freight (semi) rail/road terminals and support systems. Railways have been developing independently for more than 200 years, cars for more than 125 years. By combining road and rail transport into one transportation system, we can obtain an optimal land transport system that eliminates almost all of the problems of current transport better than other currently developed transport solutions. Based on new combination of existing technologies and infrastructure only.

A passenger-oriented method integrating perimeter control with bus dispatch is proposed for solving passenger dispersion problem under metro interruption. Its essential to use perimeter control to prevent the neighborhood of the affected road network from congestion by postponing or transferring the surplus cars out of the protected area. For executing perimeter control, the theoretical tool of Macroscopic Fundamental Diagram (MFD) is used to interpret the relationship between private cars and buses which is required for transfer passengers from the targeted closed metro station. The framework based on MPC method is used to optimize the control parameters (e.g. the number of vehicles or dynamic split rates allowed to enter protected network). Furthermore, signal timing of the intersections on the boundary of protected network is studied considering the optimized split rates. Finally, the real road network around Zhujiang New Town station of Metro Line No. 5 in Guangzhou is modeled by Aimsun. A number of simulation experiments are conducted, and the efficiency of the proposed management strategy is verified by comparing with different existing strategies. To the best of our knowledge, its the first effort to solve passenger dispersion under subway interruption using the idea of perimeter control based on MFD.

Congestion observed in the transportation systems aggregates various amounts of uncertainty at different levels, making it challenging to achieve a clear understanding regarding empirically-observed traffic instabilities. In the literature, lane changing has been frequently indicated as an important factor of oscillations under high-density flows (Ahn and Cassidy, 2007; Laval and Leclercq, 2010; Zheng, 2014). But oscillations appear also on one-lane roads when a small fluctuation caused by single-vehicle amplifies eventually leading to a jam (Stern et al., 2018). Modelling these phenomena is not a simple task. At microscopic level, for example, many car-following (CF) models, which explicitly reproduce the dynamics governing the actions of the driver-vehicle system while following another vehicle, are not able to properly reproduce traffic oscillation in a satisfactorily way. For this reason, new CF models are regularly introduced with increasing levels of complexity. However, in many cases, this does not lead to an improved understanding of traffic phenomena or to a better traffic simulation as it is difficult to have sufficient data to correctly characterize them.

In this paper, we propose an optimization model to select the charging locations of an automated electric taxi (AET) system. The service provided by this AET system is a seamless door-to-door service connected to the train station, which helps improve the last mile transport. We individualize the vehicles instead of treating them as a flow to track the remaining battery level of each AET. Two types of charging are considered containing depot charging with lower charging speed and opportunity charging with higher charging speed. We formulate a mixed-integer programming model with linear constraints to optimize the locations of depot charging and opportunity charging according to the objective function of maximizing the number of satisfied requests. The proposed model is applied to the case study city of Delft, the Netherlands with the travel demand generated by the Delft Zuid train station. Results show that the charging scheme with two types of charging can provide sufficient electrical energy for shared use AETs to serve passengers last mile travel demand.

Real-time systems require up-to-date information. Measurement signals in the power train of Electric Vehicles (EVs) are however often received with individual time delays due to the distributed architecture of the power train. Our idea is to compensate the time delays by predicting each signal from the last received value until the present time step. In this work, we evaluate 5 state-of-the-art algorithms and 2 naive methods for time series prediction. We execute all algorithms on real power train data of EVs and compare the results. Our evaluation focuses on run-time and accuracy. All methods achieve a prediction error rate of less than 5 %. As expected, the benchmark naive method is the fastest. Surprisingly, it retrieves comparable results to Exponential Smoothing. BATS and TBATS are the slowest methods. Nevertheless, they achieve the best accuracy, but suffer from outliers. Auto-Regressive Integrated Moving Average (ARIMA) achieves the smallest Mean Absolute Percentage Error (MAPE) and thus the best compromise between outliers and accuracy of all algorithms. Additionally, to further improve the accuracy, we investigate Additionally, to further improve the accuracy, we investigate the benefits of combining predictions of different algorithms.

The design of hybrid electric vehicles based on real-world driving conditions becomes increasingly important due to strict legislation and complexity of future powertrain concepts. Therefore, component sizes should be optimized not only with respect to fuel consumption but also regarding real-world load conditions. Since e.g. a proper design of the cooling system of electrical components requires consideration of time-related power demand, this paper proposes a novel concept of incorporating time frame-based load analysis (TFBA) into driving cycle synthesis. The synthesis is carried out within a two-layer optimization framework where the first layer considers statistical features and the second considers quantities directly related to a vehicles' load implication. By taking into account Markov chain theory, a class frame is derived which then serves as design space for optimizing a sequence of micro-trips with respect to meeting target criteria based on the concepts of mean tractive force and TFBA. To prove practicality, an exemplary driving cycle is synthesized and the method is validated within a parameter study. Results show that the synthetic driving cycle is representative with respect to the considered target criteria and moreover statistical quantities used for validation are within an error tolerance of 5%.

Is a roundabout still a good solution in the era where intelligent intersection control using vehicle connection (i-TLC), is in upswing? To answer this question, the capacity of i-roundabouts, where the infrastructure communicates with the vehicles (I2V), is determined analytically. The roundabouts considered are single-lane roundabouts and turbo roundabouts (a spiral multi-lane roundabout with reduced number of conflicts). A macroscopic approach explores the capacity gain that can be achieved by taking into account the necessary safety margins with regard to headways and gaps. Furthermore, it is assumed that by using I2V the speed, headway, and also the driving curve of the vehicles can be controlled. For roundabouts with speeds lower than 36 km/h, the conclusions are that: On a single lane roundabout, roughly a doubling of the capacity can be achieved. On a turbo roundabout the capacity gain can be surprisingly much higher (about a factor 2.5). This is due to the possibility of gap synchronization on the double-lane segments.

As automated vehicles become increasingly common on the road, the call for an appropriate preparation for its drivers is becoming more urgent. Expert opinions and insights have been acquired via a focus group discussion with eleven Dutch driving examiners to assist in inventorying what types of preparations are needed. The concept of meaningful human control (MHC) as an integral part of the discussion lead to consensual findings regarding ADAS functionality and the drivers tasks, as well as discussion topics on driver training and levels of automation. It was concluded to have more research into human factors to safeguard proper control over automated vehicles.

Accurately modeling the realistic and unstable traffic dynamics of human-driven traffic flow is crucial to being able to to understand how traffic dynamics evolve, and how new agents such as autonomous vehicles might influence traffic flow stability. This work is motivated by a recent dataset that allows us to calibrate accurate models, specifically in conditions when traffic waves arise. Three microscopic car-following models are calibrated using a microscopic vehicle trajectory dataset that is collected with the intent of capturing oscillatory driving conditions. For each model, five traffic flow metrics are constructed to compare the flow-level characteristics of the simulated traffic with experimental data. The results show that the optimal velocity-follow the leader (OV-FTL) model and the optimal velocity relative velocity model (OVRV) model are both able to reproduce the traffic flow oscillations, while the intelligent driver model (IDM) model requires substantially more noise in each driver's speed profile to exhibit the same waves.

In this paper we propose a novel observer-based method for anomaly detection in connected and automated vehicles (CAVs). The proposed method utilizes an augmented extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model with time delay, where the leading vehicle's trajectory is used by the subject vehicle to detect sensor anomalies. We use the classic $chi^2$ fault detector in conjunction with the proposed AEKF for anomaly detection. To make the proposed model more suitable for real-world applications, we consider a stochastic communication time delay in the car-following model. Our experiments conducted on real-world connected vehicle data indicate that the AEKF with $chi^2$-detector can achieve a high anomaly detection performance.