Aggregated Modeling for Multimodal Traffic Flow and Dispatching Control in Urban Road Networks with Ride-Sharing Services

Abstract

As ride-sharing emerges as a new mobility choice, modeling and control of large-scale multimodal systems in automated and shared environment recently has received significant attention. This paper developed a modeling approach for capturing the demand–supply relationship and the traffic flow dynamics in such ride-sharing systems at an aggregated network level. Private cars, taxis, and single- and multioccupancy ride-sharing vehicles are considered. A macroscopic fundamental diagram–based traffic flow model was constructed to describe the spatiotemporal physics of traffic. Multimodal meeting functions are utilized for passenger–vehicle matching in the shared transportation system. The proposed model enabled us to formulate an optimization model for region-level dispatching control strategies and relocating taxis and ride-sharing vehicles for different objectives. The results of experimental study reveal that (1) the proposed model can reproduce traffic dynamics and multimodal interactions under various traffic conditions, travel demands, and service intensities; and (2) the developed control theory–based dispatching strategies can improve the efficiency of all modes in the shared transportation system, and reduce the travel cost for riders and promote the level of service for passengers.