Citation

Huang K, Han X, An K, Liu Z. Transp. Res. D Trans. Environ. 2024; 132: e104221.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trd.2024.104221

PMID

unavailable

Abstract

Shared Autonomous Vehicle (SAV) is an emerging travel mode that can reduce parking spaces and accelerate urban sprawl. The time-space varying demand leads to the imbalance between travel demand and vehicle supply. Using pricing to affect clients' mode choice is a key method for addressing the imbalance problem, which includes long-term pricing and short-term allowance. Hence, in this paper, we propose an innovative method to enhance the SAV utilization rate while considering stochastic demand: fixed price on population mode choice and real-time allowance on personal departure time choice. A mixed integer nonlinear program is formulated to maximize the total profits of SAV operators. To solve this model, a customized gradient search algorithm is proposed. A case study is conducted in Suzhou Industrial Park, China. It reveals the impacts on travel demand and departure time choices and discusses the impacts and policy-making for SAV applications.

Keywords

Choice behavior; Imbalance problem; Shared autonomous vehicle; Short-term and long-term impacts; Spatiotemporal varying demand