Abstract
Ride-sharing services, that have been growing in recent years with the start of network service companies, will be further enhanced by the recently emerging trend of applications for autonomous vehicles for future traveler mobility. One fundamental question that transportation managers should address is how to capture the endogenous traffic patterns involving the new and uncertain elements facing future transportation planning and management. By concentrating on one ideal system optimal (SO) scenario, in which (i) all vehicles are autonomous, or can be centrally guided and (ii) all passengers’ pickup/drop-off trip requests can be given at the beginning, this paper aims to integrate travel demand, vehicle supply, and limited infrastructure. Available ride-shared and autonomous vehicles, from different (real/virtual) depots, can be optimally assigned to satisfy passengers’ trip requests, while considering the endogenous congestion in capacitated networks. A number of decomposition approaches are adopted in this research. Focusing on this primal problem, we propose an arc-based vehicle-based integer linear programming model in space-time-state (STS) networks, which is solved by Dantzig-Wolfe decomposition. From the perspective of dynamic traffic assignment, a space-time-state (STS) path-based flow-based linear programming model is also provided as an approximation according to the mapping information between vehicle and passenger, and between a vehicle and the space-time arc in each STS path in our priori-generated column pool. Finally, numerical experiments are performed to demonstrate our decomposition approaches and their computation efficiency. From our preliminary experiments, we have a few interesting observations: (i) without considering road congestion, the network performance/efficiency could be overestimated; (ii) passengers’ required pickup and drop-off time windows could be a buffer to mitigate road congestion, without impacting system performance; (iii) the ride-sharing service could reduce the total transportation system cost under centralized control.
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