Short-term and long-term impacts of shared autonomous vehicle choice behavior.

• Analyze the spatiotemporal variation of shared autonomous vehicle demand. • Consider demand uncertainty in mode choice and departure time choice. • Establish a two-stage joint optimization with the travel price and allowance. 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. [ABSTRACT FROM AUTHOR]