Pareto optimal regulatory strategies for coupled ridesourcing and taxi markets with impatient passengers

This study develops a multi-objective bi-level programming model to identify the Pareto optimal combined regulatory strategy that simultaneously accounts for passengers, taxi drivers, ridesourcing vehicle (RSV) drivers, and the transportation network company (TNC). The upper level determines four regulatory controls, including the RSV fleet cap, taxi fare rate, government-guided RSV fare rate, and TNC wage rate floor, while the lower level obtains the steady-state market performance, which is formulated as a fixed-point problem and approximated through iterative agent-based simulations. To solve the model, a multi-objective Bayesian optimization algorithm is developed. Based on the DiDi dataset collected from Hangzhou City in 2018, our experiments demonstrate that no regulatory strategy can simultaneously benefit all stakeholders. If the government considers maximizing vehicle utilization as a secondary criterion, then it should decrease the RSV fleet cap, impose higher fare rates, and allow the TNC to pay lower wages, compared with the benchmark scenario. Furthermore, it is recommended that the government should avoid regulations that primarily favor passengers or the TNC, as our results reveal that such policies could harm other stakeholders and reduce vehicle utilization by up to 11.6%. Finally, if passengers’ impatience is overlooked, taxi drivers may lose 23.3% of potential profits.