Analytical Techniques for Studying On-Demand Shared-Use Mobility

智能移动设备的激增催生了按需经济，旨在以非常低的交易成本有效地将消费者和供应商聚集在一起。作为按需经济的典型例子，优步和Lyft等网约车公司正在改变出租车行业和我们在城市的出行方式。这些公司提供叫车应用，智能地寻找私家车车主，让他们为乘客提供有利可图的出租车服务。这些公司取得了成功，但也引发了争议。之所以出现这种争议，是因为对出租车实施了价格、进入和服务质量方面的监管，而对网约车公司施加的监管要求相对较少。不公平竞争尤其受到出租车司机和他们的雇主的质疑。因此，网约车外包服务的成功让人对监管出租车行业的有效性产生了怀疑；它挑战了该行业监管背后的前提。这笔赠款将开发分析出租车市场结构和竞争的方法和工具，并通过网约车外包服务获得对其监管的见解。这笔拨款将为许多城市的政府机构提供及时的支持，以更好地了解网约车公司的影响和影响，并制定政策来指导其部署。研究结果还可以为其他类型的按需经济以及其他新兴的城市移动服务(如汽车共享和拼车)的分析和管理提供参考。这笔助学金将涉及所有级别的学生和传统上代表人数较少的学生，创建供课堂使用的互动虚拟环境。此外，还将开发与按需经济和创新城市交通服务有关的材料，以加强现有课程。这项研究将在两个主要方面进行。第一个目标是建立一个分析框架，在简化的总体或宏观环境下，研究出租车市场与网约车外包公司的最优监管制度。通过一个外生匹配函数来表示乘客和司机之间的匹配，我们将研究不同市场结构下总出租车市场的均衡特性，以得出是否有必要或如何监管市场的见解。聚合模型将扩展到网络和动态环境中，以更好地捕捉出租车服务供需的空间和时间变异性。第二个推力开发了基于代理的模拟和模拟试验台，以更好地了解网约车外包平台的工作机制，验证从开发的模型获得的分析结果，并针对分析上难以解决的更复杂的情况测试各种法规和政策。如果成功，这笔赠款将促进对出租车市场的了解和分析能力。它将丰富交通系统分析、网络建模和基于智能体的仿真建模的文献。

The proliferation of smart mobile devices has given rise to on-demand economy, which aims to effectively bring together consumers and suppliers with very low transaction costs. As a typical example of on-demand economy, ride-sourcing companies, such as Uber and Lyft, are transforming the taxi industry and the way we travel in cities. The companies provide ride-hailing applications that intelligently source private car owners who drive their own vehicles to provide taxi services for profit to riders. These companies have been successful, but have created controversy. This controversy arises due to regulations in terms of price, entry and service quality that are imposed on the taxis while comparatively fewer regulatory requirements have been imposed on ride-sourcing companies. Unfair competition is argued particularly by cab drivers and their employers. The success of ride-sourcing services has thus created doubt about the efficacy of regulating the taxi industry; it challenges the very premise behind regulation in this industry. This grant will develop methodologies and tools for analyzing the structure and competition of taxi markets with ride-sourcing services and deriving insights on their regulation. The grant will provide timely support for the government agencies of many cities to better understand the impacts and implications of ride-sourcing companies and develop policies to guide their deployment. The research results can also shed light on the analysis and management of other types of on-demand economy and other emerging urban mobility services such as car sharing and ridesharing. This grant will involve students at all levels and traditionally underrepresented students, creating interactive, virtual environments for in-class use. Additionally, materials related to an on-demand economy and innovative urban mobility services will be developed to enhance existing courses. The research will be conducted in two main thrusts. The first aims to develop an analytical framework to investigate optimal regulation regimes of a taxi market with ride-sourcing companies under simplified aggregate or macroscopic settings. Representing the matchings between customers and drivers by an exogenous matching function, the equilibrium properties of an aggregate taxi market will be examined under different market structures to derive insights on whether it is necessary or how to regulate the market. The aggregate model will be extended to network and dynamic contexts to better capture the spatial and temporal variability of the supply and demand of taxi services. The second thrust develops an agent-based simulation and a simulated test bed to provide a better understanding of the working mechanisms of ride-sourcing platforms, validate the analytical results obtained from developed models, and test various regulations and policies for more complex cases that are analytically intractable. If successful, this grant will advance the knowledge of and analysis capabilities for taxi markets. It will enrich the literature of transportation systems analysis, network modeling and agent-based simulation and modeling.