Locating Parking Hubs in Free-Floating Ride Share Systems via Data-Driven Optimization

This paper presents a data-driven study on locating parking hubs in free-floating ride share systems. Recently, there has been an increase in free-floating ride share systems, where users are allowed to pick up and drop off shared vehicles anywhere in the service area. However, these systems can suffer from significant demand and supply imbalance, while certain parking habits may disturb the desired city layout. A potential solution is to allocate parking hubs in an optimal manner to regulate the behaviour of the users. This paper develops a scenario optimization model for finding the optimal locations of parking hubs. The model determines the capacities and locations of the parking hubs, while considering the uncertainty of parking demand and points of interest in the area. We design an algorithm that combines the idea of Constraint-and-Column Generation and the Alternating Direction Method of Multipliers (ADMM) algorithm to solve the optimization problem in a decentralized manner, and accompany the computed solution with a probabilistic performance certificate. We also compare the adopted approach with respect to a worst case paradigm both in terms of computational cost and in terms of conservatism of the resulting solution. Numerical results show that the proposed method leads to a less conservative performance compared to the worst case method, and reduces the computational cost compared to the classical ADMM.