Freight Virtual room 4 Presentation
Nov 05, 2020 08:30 AM - Mar 01, 2021 09:30 AM(Europe/Amsterdam)
20201105T0830 20201105T0930 Europe/Amsterdam S3-1.3 - Freight modeling and simulation (Freight) Virtual room 4 IEEE- Forum ISTS2020 n.fontein@tudelft.nl
35 attendees saved this session
Using the Swedish Commodity Flow Survey in freight transport research Watch Recording 0
08:30 AM - 08:50 AM (Europe/Amsterdam) 2020/11/05 07:30:00 UTC - 2021/03/01 07:50:00 UTC
Presenters Gerard De Jong
Significance
Co-Authors
SL
Samuel Lindgren
Swedish Road And Transport Research Institute (VTI)
Simulating urban freight flows in e-grocery scenarios accounting for consumer heterogeneous preferences Watch Recording 0
Undecided 08:50 AM - 09:10 AM (Europe/Amsterdam) 2020/11/05 07:50:00 UTC - 2021/03/01 08:10:00 UTC
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.
Agent-based urban freight simulator for evaluating logistics solutions - SimMobility Freight - and data collection approach Watch Recording 0
Undecided 09:10 AM - 09:30 AM (Europe/Amsterdam) 2020/11/05 08:10:00 UTC - 2021/03/01 08:30:00 UTC
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).
Presenters Takanori Sakai
Singapore-MIT Alliance For Research And Technology
Co-Authors
AA
André Alho
Singapore-MIT Alliance For Research And Technology
BB
Bhavathrathan B. K.
Indian Institute Of Technology Palakkad
GD
Giacomo Dalla Chiara
Department Of Civil And Environmental Engineering, University Of Washington
RG
Raja Gopalakrishnan
Chief Data Scientist, Centre For Railway Information Systems, New Delhi
PJ
Peiyu Jing
Intelligent Transportation Systems Lab, Department Of Civil And Environmental Engineering, Massachusetts Institute Of Technology
MB
Moshe Ben-Akiva
Intelligent Transportation Systems Lab, Department Of Civil And Environmental Engineering, Massachusetts Institute Of Technology,
CC
Cheng Cheng
Singapore-MIT Alliance For Research And Technology,
Z
Zhao Fang
Singapore-MIT Alliance For Research And Technology
LC
Lynette Cheah
Engineering Systems And Design, Singapore University Of Technology And Design
TH
Tetsuro Hyodo
Department Of Logistics And Information Engineering, Tokyo University Of Marine Science And Technology
YH
Yusuke Hara
Singapore-MIT Alliance For Research And Technology
Significance
University of Catania
Singapore-MIT Alliance for Research and Technology
 Michiel De Bok
Delft University of Technology
Dr. Charlotte Fléchon
PTV Planung Transport Verkehr AG
Technical University of Munich
 Anna Reiffer
Karlsruhe Institute of Technology (KIT)
Mr. Giovanni Calabrò
University of Catania
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