In the United States, car ownership is still the best predictor of upward social mobility. Those without a car are disadvantaged in accessing jobs, health care, education, and basic services, including buying groceries. Public transportation has the potential to mitigate congestion and provide environmentally friendly and cost-effective mobility. Existing systems, however, are often plagued by the "first/last mile" problem, i.e., the inability to take travelers all the way from their origin to their destination. As a result, travelers who can afford them prefer private vehicles, creating congestion and harmful emissions. New mobility services such as Uber and Lyft have improved transportation for some population segments by exploiting ubiquitous connectivity to match riders and potential drivers. Unfortunately, they increase congestion and emissions, prompting some cities to limit their numbers. In addition, those services are of limited use to low income citizens and may even lure affluent residents away from transit systems, reducing revenue. In contrast, this Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) Program award explores the concept of On-Demand Multimodal Transit Systems (ODMTS). Being multimodal, ODMTS combine on-demand mobility services that serve low-density regions with high-occupancy vehicles (buses or trains) traveling along high-density corridors. They differ from micro-transit solutions by planning, operating, and optimizing transit systems holistically, using state-of-the-art optimization technology and machine learning. As a result, they have the potential to transform accessibility across population segments, decreasing inequalities in transportation and providing a sustainable transportation model for American cities and beyond.
To realize this vision, this award researches the data and decision science needed to engineer ODMTS for large, congested cities. This requires a step change in our ability to plan, operate, and optimize ODMTS, which are complex, socio-technical systems deployed over a sophisticated infrastructure. To achieve this objective, the award explores five research threads that address the following, high-level issues: (1) the delivery of scalable optimization and machine learning algorithms for designing the multimodal networks at the core of ODMTS, (2) accurate forecasting of ridership demand in ODMTS and integration of the resulting forecasting models into optimization models for network design, (3) generalization of network design optimization to account for and mitigate congestion, (4) co-design of ODMTS and infrastructure improvements to maximize the performance of transit systems, using concepts such as complete streets and context-sensitive solutions, and (5) the incremental integration of autonomous vehicles into ODMTS as they become available.
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.
