Modelling and managing ridesharing in a multi-modal network with an aggregate traffic representation: a doubly dynamical approach
Ridesharing has attracted growing attention for its potential to improve city mobility as a supplement to driving a car alone and to bring significant environmental and economic benefits. With the development of mobile communication technologies, emerging ridesharing platforms have facilitated the adoption of ridesharing by reducing the matching/meeting friction between drivers and riders. In this context, how to appropriately model and manage travelers' choices with ridesharing in place, the resultant traffic dynamics, and the potential impact on congestion becomes very important.
This research investigates the ridesharing problem within a doubly dynamical framework, where day-to-day and within-day traffic variations and the time-varying performance of ridesharing are modeled. Specifically, the time-dependent ridesharing matching process and waiting of travelers are incorporated. This research also develops and compares two time-dependent congestion pricing schemes: pricing solo-driving vehicles only and pricing all vehicles including ridesharing vehicles. The pricing levels under each scheme can be determined either through an adaptive adjustment mechanism from period to period driven by observed traffic conditions, or through solving a bi-level optimization problem. This research provides theoretical tools and policy implications for traffic management in a multi-modal network with ridesharing services.
Paper: Wei, B., Saberi, M., Zhang, F., Liu, W., & Waller, S. T. (2020). Modeling and managing ridesharing in a multi-modal network with an aggregate traffic representation: a doubly dynamical approach. Transportation Research Part C: Emerging Technologies, 117, 102670. https://doi.org/10.1016/j.trc.2020.102670
For more information, please contact
Dr Wei Liu