From data to decisions in urban transit and logistics

Abstract

Urban transit and city logistics have undergone major changes in recent years, including increased peak congestion, shrinking mass transit ridership, and the introduction of ride-sharing and micro-mobility platforms. At the same time, widespread data collection offers transit agencies insight into their riders in unprecedented detail. In this setting, data has the potential to inform decision-making and make meaningful impact on problems of great public interest. This thesis concerns data-driven decision-making for public transit systems, and spans topics from demand estimation to the design and operation of fixed-route systems and paratransit. The first chapter is concerned with origin-destination demand estimation for public transit. Our aim is to estimate demand using aggregated station entrance and exit counts, which can be modeled as the problem of recovering a matrix from its row and column sums.

We recover the demand by assuming that it follows intuitive physical properties such as smoothness and symmetry, and we contrast this approach both analytically and empirically with the maximum entropy method on real-world data. The next two chapters then use this demand data to inform strategic transit planning problems such as network design, frequency-setting, and pricing. These problems are challenging alone and made even more difficult by the complexity of commuter behavior. Our models address operator decision-making in the face of commuter preferences, and our approaches are based on column generation and first-order methods in order to model complex dynamics while scaling to realistic city settings. Finally, we explore tactical decision-making for paratransit. Paratransit is a government-mandated service that provides shared transportation for those who cannot use fixed routes due to disability.

Although paratransit is an essential safety net, it is also expensive and requires large government subsidies. These financial difficulties motivate us to develop large-scale optimization algorithms for vehicle routing in paratransit. We provide an optimization-based heuristic approach to servicing paratransit requests subject to labor constraints; this approach shows strong performance while also being tractable for several thousand daily requests..