CAREER: Algorithmic Foundations for Demand-Responsive Transit Systems - Creating More Equitable and Sustainable Cities through Better Transit

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

This Faculty Early Career Development (CAREER) project will address fundamental research questions related to designing and operating transit-centric transportation systems, with the aim of enabling an efficient, sustainable and equitable transportation system for all. While the past decade has seen enormous advances in transportation technologies such as ridesharing and self-driving cars powered by, for example, artificial intelligence, mobile phone adoption and new business models, it remains unclear whether these innovations alone can lead us toward a future that is sustainable and equitable.

This project argues that fundamental progress in this regard is best achieved via hybrid transit systems, services that seamlessly integrate the efficiencies of mass transit with agile, demand-responsive modes related to ridesharing. The technical focus will be on algorithms for designing and operating such systems, an area with key research gaps. The research will be conducted through the lens of Algorithm Engineering, which focuses on developing theoretical insights from successful data-driven and heuristic approaches, and heuristics from theory.

Collaborations with stakeholders, such as transit agencies, technology providers, community groups, and policy makers will enable an understanding of practical and societal needs, model calibration using real-data, and validation through simulation and deployments. The project aims to broaden the renewed national focus on transit infrastructure to innovations in service modes, and will involve community outreach and education efforts targeting high school students, college students and public agencies.

The research will incorporate technical ideas from Civil Engineering, Operations Research and Computer Science to develop new methodologies and train students with cross-disciplinary expertise. The project aims to make fundamental intellectual contributions for enabling the realization of hybrid transit systems, spanning four research thrusts: 1) Quantifying the value of integrating agile, demand-responsive systems with mass transit via formalizing a new metric, the Value of Dynamism, and designing efficient techniques based on two-stage stochastic optimization for computing it; 2) Developing new models and algorithms for hybrid transit network design via approximation algorithms and data-driven heuristics; 3) Integrating pricing and social welfare analysis with hybrid network design, via compact mixed integer linear programming (MILP) formulations, and using these methods to understand the impact of service design and policy decisions (e.g., subsidies) on societal goals (e.g., equity); and 4) Developing fast, scalable, passenger-matching and routing algorithms for the demand-responsive component of hybrid transit systems—by formulating novel relaxations of the classical integer linear programming approach that can efficiently integrate information about future demand (i.e., be non-myopic).

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.