Ridesharing is one of the effective urban traffic supply and demand management policies to reduce car ownership and mitigate traffic congestion. The origin-destination (OD) based surge pricing strategy is widely adopted by ridesharing service operators in practice due to its fairness and effectiveness. In this study, we aim to investigate the ridesharing user equilibrium (RUE) problem for an urban transportation network under the OD-based surge pricing strategy. We first build a variational inequality (VI) model for the proposed RUE problem. In particular, we explicitly formulate the necessary ride-matching constraints for the participants of multiple ridesharing services and rigorously demonstrate the existence and uniqueness of the RUE solution under some mild conditions. A parallel self-adaptive projection method (PSPM) incorporating column generation is developed to find an RUE solution for the large-scale problems. Finally, numerical experiments are conducted to provide valuable insights and examine the effectiveness of the proposed solution method. The results quantitatively show that the ridesharing under the OD-based surge pricing strategy reduces not only the travel cost for travelers but also the deliberate detours. Traffic congestion is significantly mitigated by ridesharing. Moreover, the proposed solution method has satisfactory computational efficiency for solving the large-scale problems.

乘车共享是减少车辆拥有量和缓解交通拥堵的有效城市交通供求管理政策之一。基于出行目的地（OD）的激增定价策略由于其公平性和有效性而在实践中被乘车共享服务运营商广泛采用。在这项研究中，我们旨在调查基于OD的激增定价策略下的城市交通网络乘车共享用户均衡（RUE）问题。我们首先为提出的RUE问题建立了变分不等式（VI）模型。特别是，我们明确为多个拼车服务参与者制定了必要的拼车限制条件，并在某些温和条件下严格证明了RUE解决方案的存在性和唯一性。开发了一种结合列生成的并行自适应投影方法（PSPM），以找到针对大规模问题的RUE解决方案。最后，进行了数值实验，以提供有价值的见解并检验所提出的解决方法的有效性。结果定量地表明，基于OD的激增定价策略下的乘车共享不仅减少了旅行者的旅行成本，而且还减少了故意的弯路。拼车大大缓解了交通拥堵。此外，所提出的解决方法对于解决大规模问题具有令人满意的计算效率。结果定量地表明，基于OD的激增定价策略下的乘车共享不仅减少了旅行者的旅行成本，而且还减少了故意的弯路。拼车大大缓解了交通拥堵。此外，所提出的解决方法对于解决大规模问题具有令人满意的计算效率。结果定量地表明，基于OD的激增定价策略下的乘车共享不仅减少了旅行者的旅行成本，而且还减少了故意的弯路。拼车大大缓解了交通拥堵。此外，所提出的解决方法对于解决大规模问题具有令人满意的计算效率。