Joint optimisation of pricing and matching for mobility-on-demand ridesharing systems considering fairness and user choices

Mobility-on-demand (MoD) has the potential to revolutionise the patterns of urban mobility.
Typically, an MoD platform provides both ride-hailing and ridesharing services, exacerbating the challenges of operating a city-scale real-time MoD system. Existing studies assume that travellers are fully compliant with the platform's decisions regarding pricing and vehicle assignments, whereas, in reality, travellers can choose different modes based on monetary costs and travel experience, which may conflict with the results derived from the system perspective.
In this study, we relax this assumption by accounting for the travellers' modal choices within a framework to jointly optimise pricing and matching when both ride-hailing and ridesharing services are provided by an MoD platform. Six fairness principles are defined to characterise fair pricing for shared rides.
Computationally efficient optimisation problems are formulated accounting for coexisting ride-hailing and ridesharing services.
In numerical experiments, we assess the effectiveness of our method and compare it with state-of-the-art ones using a dataset of taxi requests for New York City. The results show that our optimisation strategy can significantly increase the service ratio and profit without sacrificing the service quality.