In recent years, Transportation Network Companies (TNC) such as Uber and Lyft have embraced ridesharing: a passenger who requests a ride may decide to save money in exchange for the inconvenience of sharing the ride with someone else and incurring a delay. When matching passengers, these services attempt to optimize cost savings. But a possible scenario is that while passenger A is matched to passenger B, if matched to passenger C then both A and C would have saved more money. This leads to the concept of “fairness” in ridesharing, which consists of finding the Nash equilibrium in a ridesharing plan. In this paper we compare the optimum plan (i.e., benefit maximized at a global level) and the fair plan in both static and dynamic contexts. We show that in contrast to the theoretical indications, the fair plan is almost optimum. Furthermore, the fairness concept may help attract more passengers to rideshare and thus further reduce vehicle miles traveled. If social preferences are included in the total benefit, we demonstrate that the optimum ridesharing plan may be unboundedly and predominantly unfair in a sense that will be formalized in this paper.

近年来，Uber 和 Lyft 等交通网络公司 (TNC) 已经接受了拼车服务：要求乘车的乘客可能会决定省钱，以换取与其他人拼车的不便并导致延误。在匹配乘客时，这些服务会尝试优化成本节约。但一种可能的情况是，虽然乘客 A 与乘客 B 匹配，但如果与乘客 C 匹配，则 A 和 C 都会节省更多钱。这导致了拼车“公平”的概念，其中包括在拼车计划中找到纳什均衡。在本文中，我们比较了静态和动态环境下的最优计划（即在全局层面上最大化的利益）和公平计划。我们表明，与理论指示相反，公平计划几乎是最佳的。此外，公平概念可能有助于吸引更多乘客乘坐拼车，从而进一步减少车辆行驶里程。如果社会偏好包含在总收益中，我们证明了最佳拼车计划可能是无限的，并且在本文中将正式化的某种意义上是不公平的。