1-4: Transit Priority
| Principal Investigator | Xiaopeng Li, Ph.D. |
| Final Report (DOI) | Available Soon |
| TRID | Available Soon |
| Policy Brief | Available Soon |
| RIP | View RIP entry |
Abstract
We will explore how to lure travelers from their cars by improving transit operations. Our initial focus will be conditional signal priority, where green lights are granted to buses arriving at signalized intersections as needed. The USF team will focus on mathematical programming models that optimize signal timing to maximize intersection performance weighted by vehicle occupancies (thus giving priority to buses with higher occupancy) in a connected and automated vehicle (CAV) environment. CAV trajectories will be jointly optimized with signal timing to reduce congestion as well as energy consumption. Besides the exact solution obtained from numerical algorithms, the model structure will be investigated to reveal asymptotical properties (e.g., via continuum approximation) and to efficiently construct approximate solutions. Additionally, the USF team will also conduct a survey of transit agencies involved in TSP efforts to understand the impact of the pandemic on these strategies. The UCB team will focus on optimizing the length of time for which green signals should be extended to accommodate late buses. Preliminary simulation shows that extending priority treatment in this way can be effective at expediting bus travel, while minimizing negative impacts to other traffic. The UCB team will also focus on problems that occur on complex route structures, for example, when multiple buses on common routes deviate from their schedules and bunch as a result. We will develop/refine an analytical model that uses real-time measurements of bus headways to nudge late-running buses back on schedule whenever needed
