关于MaaS平台应对共享出行竞争的需求
On the needs for MaaS platforms to handle competition in ridesharing mobility
摘要：
拼车已经成为一种新型的交通方式。然而，拼车缓解城市拥堵的早期承诺可能会受到一些挑战的破坏，包括作为竞争的自然结果，拟议服务的数量不断增加，车辆数量随之增加。在这项工作中，我们提出了基于优化的方法，在实时按需环境中对多个拼车公司之间的合作和竞争进行建模。最近的一个趋势依赖于解决拨号乘车问题（DARP）的综合组合，它计算将传入请求分配给车辆路线的成本，加上线性分配问题（Linear Assignment Problems，DARP），它将车辆分配给请求。虽然DARPs是在每个公司的车辆级别上解决的，但我们引入了合作和竞争的方法来解决问题。的合作模式，它可以利用移动性作为一种服务平台，解决的最优性以下密切的结果从文献中，并限制了公司所需的信息量共享。我们研究了一个现实的竞争模型如何偏离这种最优性，并提供了最坏情况下的界限。我们评估这些模型相对于一个集中式模型的纽约市出租车数据集的一周的实例。处理噪声的旅行时间估计，偏差的分配成本，并在竞争的情况下的偏好模型变量也提出和验证。计算结果表明，拼车公司之间的合作可以以这样的方式进行，以限制相对于集中式模型的服务水平的下降。最后，我们认为，竞争可以降低拼车服务的质量，特别是在客户的偏好被容纳的情况下。
英文摘要：
Ridesharing has been emerging as a new type of mobility. However, the early promises of ridesharing for alleviating congestion in cities may be undermined by a number of challenges, including the growing number of proposed services and the subsequent increasing number of vehicles, as a natural consequence of competition. In this work, we present optimization-based approaches to model cooperation and competition between multiple ridesharing companies, in a real-time on-demand setting. A recent trend relies on solving the integrated combination of Dial-A-Ride Problems (DARP), which compute the cost of assigning incoming requests to vehicle routes, plus Linear Assignment Problems (LAP), which assign vehicles to requests. While the DARPs, are solved at the level of the vehicles of each company, we introduce cooperative and competitive approaches to solve the LAP. The cooperative model, which could make use of Mobility as a Service platforms, is shown to solve the LAP to optimality following closely results from the literature, and limiting the amount of information the companies are required to share. We investigate how a realistic model of competition deviates from this optimality and provide worst case bounds. We evaluate these models with respect to a centralized model on one-week instances of the New York City taxi dataset. Model variants coping with noise in the travel time estimations, bias in the assignment costs, and preferences in the competitive case are also presented and validated. The computational results suggest that cooperation among ridesharing companies can be conducted in such a way to limit the degradation of the level of service with respect to a centralized model. Finally, we argue that the competition can lower the quality of the ridesharing service, especially in the case customer preferences are accommodated.
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