Collaborative Research: Designing Transit Systems with Public Approval through Cooperative Linear Production Games

Public transit agencies must balance the competing goals of providing efficient transportation services while ensuring access for all the communities they serve. Achieving both objectives is challenging because expanding service to underserved areas often requires resources generated by more heavily used routes, creating competing priorities among riders, taxpayers, and other stakeholders. More broadly, this tension reflects a common challenge faced by public agencies: making investment decisions that are both effective and broadly supported by the communities they affect. This award supports research to create new analytical methods that help public agencies incorporate stakeholder preferences into planning and resource allocation decisions. The resulting methods will enable the design of public investment strategies that advance policy and infrastructure goals while increasing the likelihood that they can be successfully implemented because they are broadly acceptable to affected stakeholders. This project advances mathematical programming for public-sector decision-making by incorporating stakeholder approval as an explicit requirement in the optimization process. Its methodological foundation lies at the intersection of mathematical programming and cooperative game theory, bringing together the complementary strengths of optimization and coalition-based decision analysis. The research will develop a new mathematical framework based on non-transferable utility linear production games that captures both the operational efficiency of optimization models and the preferences of affected stakeholders. The project will establish theoretical conditions that determine when stable, mutually acceptable solutions exist, develop efficient algorithms for identifying such solutions, and apply these methods to the joint design of public transit service plans and fare structures. The resulting modeling frameworks, algorithms, and analytical tools are expected to strengthen the mathematical foundations of public-sector optimization while enabling more effective and broadly supported infrastructure and public service decisions. 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.