Ride-hailing vehicle dispatching and matching strategies to prioritize and complement public transport use

Operational strategies and matching algorithms are used to ensure the availability and efficient assignment of ride-hailing services. Such operational strategies may result in services that, rather than complement traditional public transport (PT) systems, compete with them in both market (demand) and road-space use (congestion). This paper introduces and evaluates real-time vehicle dispatching strategies that focus on the prioritization of PT use and the complementarity between PT and ride-hailing in multimodal trips. Utilizing a novel two-step ride-matching algorithm, these strategies aim at decreasing travelers' wait times and motivating the use of PT. The agent-based travel demand forecasting model MATSim is used to implement and test the proposed matching strategies in the study area of Metropolitan Melbourne, Australia. The proposed strategies outperform the original MATSim strategies (which follow a first-come, first-serve approach) regarding average vehicle kilometers traveled (VKT) per ride, number of multimodal trips that use ride-hailing and PT, and the overall PT mode share. The results indicate substantial improvements across all proposed strategies, with PT mode shares increasing by 3.3% to 19.8%. A fleet size of 200 was identified as the optimal fleet size. Multimodal trips increased by 7% to 13% for all proposed strategies at this fleet size, illustrating a substantial shift towards integrated transport modes. Additionally, VKT per ride decreased by approximately 30% to 33% with the proposed strategies at this fleet size. We conclude that serving all ride-hailing requests on a first-come/first-serve basis creates system-level inefficiencies that can be overcome by prioritizing requests that cannot be served by PT and/or have an emergency nature.