Optimal matching for ridesharing systems with endogenous and flexible user participation
研究了拼车平台与用户之间的博弈，用户可灵活选择角色（乘客、司机或两者皆可），平台通过最优匹配最大化社会福利，并用芝加哥真实数据验证了用户灵活性对系统性能的影响。
The performance of ridesharing systems is intricately entwined with user participation. To characterize such interplay, we adopt a repeated multi-player, non-cooperative game approach to model a ridesharing platform and its users’ decision-making. Users reveal to the platform their participation preferences over being only riders, only drivers, flexible users, and opt-out based on the expected utilities of each mode. The platform optimally matches users with different itineraries and participation preferences to maximize social welfare. We analytically establish the existence and uniqueness of equilibria and design an iterative algorithm for the solution, for which convergence is guaranteed under mild conditions. A case study is conducted with real travel demand data in Chicago. The results highlight the effect of users’ flexibility regarding mode preferences on system performance (i.e., the average utility of users and the percentage of successful matches). A sensitivity analysis on the level of subsidy and the distribution of utility between matched riders and drivers shows that uneven distributions of utility may lead to a higher percentage of successful matches. Additional insights are provided on the effect of a user’s origin and destination locations on their role choice and likelihood to be matched.