Analytical Techniques for Studying On-Demand Shared-Use Mobility

研究按需共享使用移动性的分析技术

• 批准号: 1740865
• 负责人: Yafeng Yin
• 金额: $ 35.49万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740865&HistoricalAwards=false
• 关键词: Analytical; Techniques; Studying; Demand; Shared

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.

智能移动的设备的普及带来了按需经济，其目的是以极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，Uber和Lyft等叫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程序，智能地寻找驾驶自己车辆的私家车主，为乘客提供出租车服务以获取利润。这些公司取得了成功，但也引起了争议。这一争议的产生是由于对出租车实施的价格、准入和服务质量方面的规定，而对拼车公司实施的监管要求相对较少。出租车司机和他们的雇主尤其认为不公平竞争。因此，网约车服务的成功使人们对出租车行业监管的有效性产生了怀疑;它挑战了该行业监管背后的前提。这笔赠款将开发方法和工具，用于分析出租车市场的结构和竞争，并获得有关其监管的见解。这笔赠款将为许多城市的政府机构提供及时的支持，以更好地了解乘车外包公司的影响和影响，并制定政策以指导其部署。研究结果还可以为其他类型的按需经济和其他新兴城市移动服务（如汽车共享和乘车共享）的分析和管理提供帮助。这笔赠款将涉及各级学生和传统上代表性不足的学生，创造互动，虚拟环境，供课堂使用。 此外，还将开发与按需经济和创新城市交通服务相关的材料，以加强现有课程。这项研究将在两个主要方面进行。第一个旨在开发一个分析框架，以研究在简化的总体或宏观环境下有网约车公司的出租车市场的最佳监管制度。通过一个外生匹配函数来描述乘客和司机之间的匹配关系，我们将在不同的市场结构下考察出租车市场的均衡特性，从而得出是否有必要或如何监管市场的见解。综合模型将扩展到网络和动态环境，以更好地捕捉出租车服务供求的空间和时间变化。第二个重点是开发基于代理的模拟和模拟测试平台，以更好地了解乘车外包平台的工作机制，验证从开发的模型中获得的分析结果，并测试各种法规和政策，以解决分析上难以解决的更复杂的问题。如果成功，这笔赠款将提高出租车市场的知识和分析能力。它将丰富运输系统分析，网络建模和基于代理的仿真和建模的文献。