[论文解读] Quantifying the uneven efficiency benefits of ridesharing market integration

Xiaohan Wang, Zhan Zhao+3 more|arXiv (Cornell University)|Feb 5, 2023

Transportation and Mobility Innovations被引用 5

一句话总结

本研究将共享网络框架扩展至量化多个交通网络公司（TNCs）之间拼车市场整合所带来的不均衡效率收益，基于曼哈顿地区真实TNC行程数据进行分析。研究发现，市场整合可使拼车效率提升13.3%（占每日TNC车辆行驶时长的5%），且收益差异显著取决于需求密度、时空分布不均性以及各TNC特有的属性，如TNC内部需求聚集程度。

ABSTRACT

Ridesharing is recognized as one of the key pathways to sustainable urban mobility. With the emergence of Transportation Network Companies (TNCs) such as Uber and Lyft, the ridesharing market has become increasingly fragmented in many cities around the world, leading to efficiency loss and increased traffic congestion. While an integrated ridesharing market (allowing sharing across TNCs) can improve the overall efficiency, how such benefits may vary across TNCs based on actual market characteristics is still not well understood. In this study, we extend a shareability network framework to quantify and explain the efficiency benefits of ridesharing market integration using available TNC trip records. Through a case study in Manhattan, New York City, the proposed framework is applied to analyze a real-world ridesharing market with 3 TNCs$-$Uber, Lyft, and Via. It is estimated that a perfectly integrated market in Manhattan would improve ridesharing efficiency by 13.3%, or 5% of daily TNC vehicle hours traveled. Further analysis reveals that (1) the efficiency improvement is negatively correlated with the overall demand density and inter-TNC spatiotemporal unevenness (measured by network modularity), (2) market integration would generate a larger efficiency improvement in a competitive market, and (3) the TNC with a higher intra-TNC demand concentration (measured by clustering coefficient) would benefit less from market integration. As the uneven benefits may deter TNCs from collaboration, we also illustrate how to quantify each TNC's marginal contribution based on the Shapley value, which can be used to ensure equitable profit allocation. These results can help market regulators and business alliances to evaluate and monitor market efficiency and dynamically adjust their strategies, incentives, and profit allocation schemes to promote market integration and collaboration.

研究动机与目标

• 量化在存在多个TNC的碎片化城市市场中，拼车市场整合所带来的效率收益。
• 识别基于现实市场特征（如需求密度和时空分布不均性）的效率收益在不同TNC间的差异。
• 评估市场竞争力强度以及TNC特异性需求集中度对个体TNC效率收益的影响。
• 提出一种基于Shapley值的公平利润分配机制，以应对收益不均问题并促进合作。
• 为监管机构和TNC联盟提供一个数据驱动的框架，用于监控、评估并动态调整整合策略。

提出的方法

• 将共享网络框架扩展至使用Uber、Lyft和Via在曼哈顿的真实行程记录，模拟多TNC市场中的拼车效率。
• 通过比较当前碎片化市场与假设完全整合市场之间的行程时长节省，估算效率收益。
• 利用网络模块度量化TNC之间的时空分布不均性，使用聚类系数衡量TNC内部需求集中度。
• 采用赫希曼-赫芬达尔指数（HHI）评估市场竞争力强度，并模拟不同TNC份额下的市场分割情景。
• 应用Shapley值计算各TNC对整合市场的边际贡献，实现公平的利润分配。
• 开展模拟实验，分析效率收益如何随需求密度、市场份额分布及不均性指标的变化而变化。

实验结果

研究问题

• RQ1 在真实城市环境中，完全整合多个TNC的拼车市场可实现多大的效率提升？
• RQ2 时空分布不均性和需求密度如何影响不同TNC间市场整合的效率收益？
• RQ3 市场份额分配如何影响整体及各TNC个体的效率收益？
• RQ4 TNC内部需求聚集程度在多大程度上影响其个体从市场整合中获得的收益？
• RQ5 如何利用Shapley值量化边际贡献，并确保协作拼车市场中的公平利润分配？

主要发现

• 曼哈顿地区若实现完全整合的拼车市场，效率可提升13.3%，相当于每日TNC车辆行驶时长的5%。
• 效率提升与总体需求密度及TNC间时空分布不均性（以网络模块度衡量）呈负相关。
• 市场份额分布更均衡的市场（HHI更低）从整合中获得更大的效率收益，表明更高的竞争强度可增强收益。
• 尽管市场份额可能较大，但TNC内部需求集中度更高的TNC从市场整合中获益更少（聚类系数更高）。
• Shapley值提供了一种稳健的方法来量化各TNC的边际贡献，从而支持公平的利润分配机制以促进合作。
• 市场碎片化导致行程匹配次优，增加绕行距离和车辆行驶时长，而市场整合可显著降低此类问题。