Ride-hail to ride rail: Learning to balance supply and demand in ride-hailing services with intermodal mobility options

Abstract

Growing spatiotemporal imbalance in supply and demand in ride-hailing services has been arousing concern. A variety of measures in the perspective of ride-hailing services per se is studied. However, approaching the imbalance problem in the broader perspective of intermodal mobility, which integrates different modes of passenger transportation in a single trip and aims to overcome limitations of any unimodal mobility, remains to be explored. This paper aims to investigate the potential of introducing intermodal mobility options to balance ride-hailing services. We first identify the importance of the availability decision on intermodal mobility options. Then we formulate the availability decision problem as a Markov decision process (MDP). Due to its convoluted system dynamics and large state space, we cast the intractable MDP into a reinforcement learning (RL) problem to approximately learn the availability policy. To stabilize the learning processes, we model the intermodal ride-hailing services as a stochastic queueing network and tailor a family of state-of-the-art RL algorithms to iteratively evaluate and improve the availability policy. Lastly, we test this optimization framework in a large-scale intermodal mobility scenario calibrated with real-world trip data. Results show that the learned availability policy can significantly dissipate riders’ queue and improve the service rates towards more balanced supply and demand.