Optimal Public Transportation Networks: Theory and Evidence.

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.