Shared autonomous vehicle management considering competition with human-driven private vehicles

Shared autonomous vehicles (SAVs) are expected to be an essential component in developing efficient and sustainable transportation systems. This paper focuses on optimizing the operational decisions of SAV systems when the mode choices between SAVs and human-driven private vehicles are considered. A time–space network flow model is formulated to optimize the vehicle assignment and relocation decisions, while a binary logit model is used to describe the nonlinear relationship between the elastic demand and its various important attributes, including the trip price and the level of service. An enhanced outer–inner approximation approach is proposed for solving the mixed-integer nonlinear programming (MINLP). For reducing the approximation error, a dynamic programming algorithm is developed to select the optimal breakpoints. The proposed modeling and solution approaches are tested based on a case study in Singapore. The authors' computational experiments show that the proposed solution approaches can efficiently obtain the optimal solutions. Numerical results reveal that a SAV system can achieve higher performance by the active relocation activities under the elastic demand. And the SAV system with a high level of service could produce a higher operating profit if users are more sensitive to the level of service.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103547
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21005295
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Guo, Hao
  • 0000-0002-6774-9388
  • Chen, Yao
  • 0000-0002-6894-7757
  • Liu, Yang
• Publication Date: 2022-3

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: 103547
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 136
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Case studies; Competition; Demand; Fleet management; Human beings; Logits; Motor vehicles; Vehicle sharing
• Geographic Terms: Singapore
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01835792
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 10 2022 5:05PM