Spatial pricing of ride-sourcing services in a congested transportation network
Spatial pricing of ride-sourcing services in a congested transportation network
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DOI：
10.1016/j.trc.2022.103777
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Fatima Afifah;Zhaomiao Guo
中科院分区：
文献类型：
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作者：
Fatima Afifah;Zhaomiao Guo
我们研究了空间定价的影响，乘坐外包服务的Stackelberg框架考虑交通拥堵。在较低的层次，我们使用组合的分配和分配方法，明确地捕捉司机的搬迁，乘客的模式选择，和所有旅行者的路线决策之间的相互作用。在上层，一个单一的运输网络公司（TNC）确定空间定价策略，以尽量减少双边市场的不平衡。我们证明了位置不平衡最小化的最优定价策略的存在性，并提出了具有可靠收敛特性的有效算法。此外，最优定价是唯一的，可以在一个凸重新求解时，等待时间是小的旅行时间相比。我们进行了不同规模的交通网络与不同的跨国公司目标的数值实验，以产生政策见解的空间定价如何影响交通系统。
We investigate the impacts of spatial pricing for ride-sourcing services in a Stackelberg framework considering traffic congestion. In the lower level, we use combined distribution and assignment approaches to explicitly capture the interactions between drivers’ relocation, riders’ mode choice, and all travelers’ routing decisions. In the upper level, a single transportation network company (TNC) determines spatial pricing strategies to minimize imbalance in a two-sided market. We show the existence of the optimal pricing strategies for locational imbalance minimization, and propose effective algorithms with reliable convergence properties. Furthermore, the optimal pricing is unique and can be solved in a convex reformulation when waiting time is small compared to travel time. We conduct numerical experiments on different scales of transportation networks with different TNC objectives to generate policy insights on how spatial pricing could impact transportation systems.