Optimal Public Transportation Networks: Theory and Evidence.

交通系统设计者面临着复杂的权衡，包括路线的数量、设计和服务频率，这些都会影响运输的安全和效率。这个研究项目将研究如何在拥挤的大城市地区设计最佳的公共交通网络。它利用高分辨率数据和几种现代经济方法来确定最佳网络结构，并讨论了最佳网络形状如何取决于城市结构和通勤者偏好。在制定最佳网络设计建议并研究其部署的过程中，该项目还将为2019冠状病毒病大流行期间及以后的高效公交系统提供宝贵见解。更广泛地说，它将为发达国家拥挤的大城市以及新兴市场的特大城市的公共汽车系统的设计特点提供见解，使全球每天数百万的公共交通乘客受益。本研究项目的成果将为城市交通设计提供投入，改善城市交通，降低通勤成本，从而提高工人生产率，促进经济更快增长。它还将改善通勤者的福祉，并有助于减缓气候变化的速度。这个由两部分组成的项目开发了一个最优城市公交系统模型，并使用项目pi为该项目收集的大量创新数据对该模型进行了估计。第一部分开发和估计了一个人口密集和拥挤的大型城市地区的公共交通出行需求模型。该项目将使用关于乘客和通勤的高分辨率微观数据，大型历史路线网络扩张引起的变化，以及pi进行的全系统随机实验，以估计通勤者对快速连接，直达连接和频繁服务等关键参数的重视程度。第二部分将使用数值优化技术来计算最优公共交通网络，并综合最优网络形状如何取决于通勤者偏好参数、城市结构和政策目标等因素。该项目通过采用严格的因果估计和研究整个城市范围内路线网络的实际经验变化，连接并扩展了交通经济学文献中的经典主题，如出行需求估计和公共交通的收益增加。本研究的定量方法和定性见解有可能为世界上发达国家和发展中国家人口稠密城市地区的公共交通网络设计提供信息。本研究项目的结果将为改善城市交通、降低交通成本、从而提高工人生产率、促进经济更快增长的设计提供投入。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Transportation system designers face complex tradeoffs around the number, design, and service frequency of routes, which impact transportation safety and efficiency. This research project will study how to design the best public transport network in congested large urban areas. It leverages high-resolution data and several modern economic methods to determine the best network structures and discusses how the best network shape depends on city structure and commuter preferences. In developing recommendations for the best network design and studying its rollout, this project will also provide valuable insights for efficient bus systems during the COVID-19 pandemic and beyond. More broadly, it will provide insights into the design features of public bus systems in congested large urban areas in the developed world as well as in emerging market mega-cities, to the benefit of millions of daily public transport riders worldwide. The results of this research project will provide inputs into urban transportation designs that improves urban transportation, decrease communing cost, hence improve worker productivity, leading to faster economic growth. It will also improve the wellbeing of commuters as well as contribute to slowing the pace of climate change. This two-part project develops a model of an optimal urban bus transit system and estimate the model with a large, innovative data collected by the PIs for the project. The first part develops and estimates a travel demand model of public transport in a large densely populated and congested urban area. The project will use high-resolution micro data on ridership and commuting, variation induced by large historical route network expansion, and a system-wide randomized experiment that the PIs have conducted, to estimate the degree to which commuters value key parameters such as fast connections, direct connections, and frequent service. The second part will use numerical optimization techniques to compute the optimal public transport network and to synthesize how the optimal network shape depends on factors such as the commuter preference parameters, city structure, and the policy objective. The project connects and expands upon classic topics in the transportation economics literature, such as travel demand estimation and increasing returns in public transport, by employing rigorous causal estimation and studying actual empirical variation in an entire citywide network of routes. The quantitative methods and qualitative insights from this study have the potential to inform public transport network design in densely populated urban areas in developed as well as developing countries around the world. The results of this research project will provide inputs into designs that will improve urban transportation, decrease communing cost, hence improve worker productivity, leading to faster economic growth.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.