Collaborative Research: Economic Modeling and Control Methods for Next Generation Carpool Services

考虑到个人选择使用拼车服务取决于有多少人选择拼车服务，这笔NSF赠款将通过探索城市交通系统中拼车的动态，为国家未来绿色交通的可持续性做出贡献。这项研究将在一系列环境中建立新的正反馈机制模型，使用来自公共机构和私营公司的数据来验证这些结果，并利用这些信息来改善对共用交通系统的控制。所获得的知识可以用来制定和完善定价、税收和其他政策战略，既可以促进共享，又可以减少城市交通拥堵。改善拼车选择还可以改善环境结果，减少车辆出行，并通过为那些无法购买或驾驶汽车但居住在离便利交通选择不太近的人提供合理的出行时间来改善社会公平。教育和外联计划包括招募学生，特别是来自任职人数不足的群体的学生，从事研究任务，并向他们提供机会，指导初级同事开展研究活动。这项研究将开发互动网络模块，向广泛的受众描述这些拼车行为。本研究将特别关注拼车过程中正反馈的两种机制。第一种是超需求，选择拼车的人比例既取决于交通拥堵，也影响交通拥堵。第二种是匹配外部性，即随着更多的人选择拼车，拼车者之间的匹配质量也会提高。这项研究将带来新的突破，利用动力系统、搜索/匹配理论和网络级交通关系的工具来建立稳健的拼车理论。为了验证这一理论，研究小组将使用从开放数据来源收集的经验数据，以及我们的行业合作伙伴。此外，促进拼车的真实政策的案例研究将为研究提供信息，并为未来的工作提供证据基础。这些研究活动将为拼车服务和叫车系统建模提供一个新的范例，这将对下一代城市交通系统产生重大影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This NSF grant will contribute to the sustainability of the nation's future green transportation by exploring the dynamics of carpooling in urban transportation systems, considering that an individual’s choice to use a carpooling service depends on how many others choose to do so as well. The research will model new mechanisms of positive feedback in a range of settings, validate these results using data from public agencies and private firms, and leverage this information for improved control of pooled transportation systems. The knowledge obtained can be used to develop and refine pricing, taxation, and other policy strategies that can both promote pooling and reduce urban traffic congestion. Improving carpool options can also improve environmental outcomes, by reducing vehicle travel, and social equity, by providing reasonable travel times to those who cannot buy or drive a car but who do not live close to convenient transit options. The education and outreach plan includes recruiting students, especially those from underrepresented groups, to work on the research tasks and provide them with opportunities to mentor junior colleagues on research activities. The research will develop interactive web modules to describe these carpooling behaviors to a wide audience. The research will specifically focus on two mechanisms of positive feedback in carpooling. The first is hyperdemand, whereby the share of people choosing to carpool both depends on and affects traffic congestion. The second are matching externalities, whereby the quality of matches among carpoolers increases as more choose to carpool. The research will break new ground by bringing to bear tools from dynamical systems, search/matching theory and network-level traffic relations to build a robust theory of carpooling. To validate the theory, the research team will use empirical data collected from open data sources, as well as our industry partners. In addition, case studies of real policies to promote carpooling will inform the research and provide an evidence base for future work. The research activities will provide a new paradigm for modeling carpooling services and ride-hailing systems that will have significant impacts for the next generation of urban transportation systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.