Optimal parking planning for shared autonomous vehicles: A two-stage stochastic programming approach

The successful deployment of shared autonomous vehicle systems relies on well-designed parking lots, which serve as both storage depots and dispatch hubs. A key challenge is the coordination between parking planning and fleet operations, exacerbated by varying land prices and demand uncertainty. To address these issues, we develop a two-stage stochastic programming model. The first stage determines the parking lot location, capacity, and initial SAV allocation, considering land prices. The second stage optimizes fleet operations through a time-space network to track vehicle movements. The model is solved using the sample average approximation method and validated through a case study in Chengdu, China. Results demonstrate that our approach consistently outperforms benchmark methods, highlighting the importance of an optimizationbased decision support for SAV parking planning. Further analysis reveals that as the fleet size increases, parking lots increasingly function as intermediate stops during operation, substantially reducing unnecessary vehicle movements and associated energy consumption.