Parking space for shared automated vehicles: How less can be more

With the anticipated introduction of self-driving vehicles, new challenges arise for urban transport- and planning authorities. This study contributes to the efforts of formulating the potential opportunities and threats stemming from the introduction of larger fleets of self-driving vehicles to cities, and what action could be taken by transport authorities to shape this introduction beneficially. In particular, the focus is put on the impact different parking management strategies can have on the performance of a fleet of shared automated vehicles providing on-demand transport services. This analysis focuses on aspects of service efficiency, externalities and service provision equity. The selected parking management strategies are tested in a large-scale activity-based simulation of a case study based on the city of Amsterdam, in which the parking facilities for SAV are digitally mapped throughout the city for different parking scenarios. The vehicles of the fleet aim at relocating to zones with high future demand, which can lead to bunching of vehicles at demand-hotspots. Parking management in the form of restricting parking facilities forces idle vehicles to spread out more evenly in the network. The authors show that this can reduce average passenger waiting times, increase service provision equity, cause less congestion and even can reduce the necessary fleet size. However, this comes at the cost of an increase in vehicle-kilometres-travelled, which reduces fleet efficiency and causes more undesired service externalities. Parking management is thus a simple, yet effective way for transport authorities to (a) determine where idle self-driving vehicles operating an on-demand transport service will be parked and (b) influence the performance of said transport service.

• Record URL:
  https://doi.org/10.1016/j.tra.2020.11.008
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0965856420307734
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Supplemental Notes:
  • © 2020 Konstanze Winter et al. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Winter, Konstanze
  • Cats, Oded
  • Martens, Karel
  • van Arem, Bart
• Publication Date: 2021-1

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 61-77
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 143
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Case studies; Demand responsive transportation; Parking; Parking demand; Simulation; Vehicle fleets; Vehicle sharing
• Geographic Terms: Amsterdam (Netherlands)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01764547
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 8 2021 11:16AM