Shared mobility Virtual room 1 Presentation
Nov 03, 2020 11:15 AM - Mar 01, 2021 12:15 PM(Europe/Amsterdam)
20201103T1115 20201103T1215 Europe/Amsterdam S1-3.1 - Shared Mobility (III) Virtual room 1 IEEE- Forum ISTS2020
41 attendees saved this session
Flow-based Routing Model of Heterogeneous Vehicles in Mixed Autonomous and Non-autonomous Zone Networks in Urban Areas Watch Recording 0
UndecidedShared Mobility 11:15 AM - 11:35 AM (Europe/Amsterdam) 2020/11/03 10:15:00 UTC - 2021/03/01 10:35:00 UTC
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.
Presenters Qiaochu Fan
TU Delft
J. Theresia Van Essen
Gonçalo Correia
TU Delft
Leveraging Behavioral Economics for Sustainable Micromobility Watch Recording 0
UndecidedShared Mobility 11:35 AM - 11:55 AM (Europe/Amsterdam) 2020/11/03 10:35:00 UTC - 2021/03/01 10:55:00 UTC
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.
Mohan Venigalla
George Mason University
Optimal detour for ridesharing on-demand transport dystems Watch Recording 0
PresentationShared MobilityShared Mobility 11:55 AM - 12:15 PM (Europe/Amsterdam) 2020/11/03 10:55:00 UTC - 2021/03/01 11:15:00 UTC
Presenters Andres Fielbaum
TU Delft
TU Delft
George Mason University
 Qiaochu Fan
TU Delft
Dr. Mohammad Miralinaghi
Research Associate
Purdue University
Dr. Xiao Liang
Delft University of Technology
Delft University of Technology
Mr. Patrick Stokkink
École Polytechnique Fédérale de Lausanne (EPFL)
+19 more attendees. View All
Upcoming Sessions
1011 visits