Suggested Citation:
Chen, Shenyang (2024)

Analysis of Ride-Sharing Fleet: Matching, Capacity, and Cooperation With Transit

. Institute of Transportation Studies, University of California, Davis, Dissertation UCD-ITS-RR-24-89

Abstract:

The last decade has seen the rise of smartphone-app-based ride-hailing services as the most disruptive innovation of the urban transportation industry. Although there have been various studies on the service quality and market behavior of e-hailing taxi services, the methodology is not readily applicable to studying ride-pool services. Unlike the e-hailing taxi service that always aims to minimize pickup time, the ride-pool service operation has more metrics to consider, including detour time and vehicle mileage savings. Yet, these metrics vary with matching strategies. This dissertation proposes an inclusive framework to analyze ride-pool services, assuming they implement the mechanism of spatial bipartite matching. An agent-based simulator is developed to validate and calibrate the models. Specifically, the analysis is carried out in three aspects.First, we apply a deductive method to demonstrate the economies of density of an ellipsoid matching algorithm. Compared to the origin-matching algorithm borrowed from the e-hailing taxi service, the numerical results demonstrate significant detour time savings despite a slight increase in pickup waiting time. Second, we apply an inductive method to evaluate an arbitrary matching strategy that applies spatial bipartite matching. Following the classic techniques in the highway capacity analysis, we propose empirical functional forms of service time versus demand, respectively, for FIFO and non-FIFO queue disciplines. After the simulation data calibration, the model can accurately predict the service time given supply and demand. Third, the ride-pool services can be easily generalized as an e-hailing demand-responsive service. Using the above service time function, we establish a hierarchical game model to explore the competitive and cooperative interactions between demand-responsive services and fixed-route transit, where the relative spatial position (RSP) design based on a grid network helps to quantify the costs for user and transit system operation explicitly. In summary, this research will help strategic planners and policymakers regulate the ride-sharing market and integrate them seamlessly into the urban public transportation service systems, providing efficient, affordable, and equitable solutions for urban travel.