Mechanism design for Mobility-as-a-Service platform considering travelers’ strategic behavior and multidimensional requirements

Mobility-as-a-Service (MaaS) is an emerging transport model which provides access to a combination of travel modes through a single platform. A MaaS operator sits between travelers and transport service providers (TSPs), acting as a broker who purchases mobility resources from individual TSPs, constructs seamless transport services, and then sells them to travelers in response to their demand. To ensure the sustainability of such platforms, the key challenge lies in matching travelers to TSPs so that travelers’ individual needs are satisfied, TSPs gain nonnegative profits and system efficiency is achieved. To solve this matching and pricing problem, travelers’ truthful valuations and travel requirements are needed, while such information is usually unknown to operators beforehand. In this study, the authors develop an auction-based online mobility resource allocation and pricing mechanism to solve this problem, taking into account travelers’ strategic behavior. The authors first propose an offline (static) mechanism using a Vickrey–Clarke–Groves (VCG) based pricing scheme to ensure incentive compatibility, individual rationality, and system efficiency. The authors then develop an online mechanism based on the dynamic learning algorithm to obtain the near-optimal solution and compare it to a customized greedy based algorithm. The authors compare both online mechanisms to the offline VCG based mechanism and theoretically prove the competitive ratios. The efficiency and performance of the proposed mechanisms are demonstrated through a numerical study.

• Record URL:
  https://doi.org/10.1016/j.trb.2023.04.004
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S019126152300067X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Ding, Xiaoshu
  • 0000-0001-8998-623X
  • Qi, Qi
  • 0000-0001-9192-8928
  • Jian, Sisi
  • 0000-0001-6743-0193
  • Yang, Hai
  • 0000-0001-5210-8468
• Publication Date: 2023-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1-30
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 173
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Algorithms; Travel behavior; Value of service pricing
• Identifier Terms: Mobility-as-a-Service (MaaS)
• Subject Areas: Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01886019
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 26 2023 12:41PM