详情

Transit Priority

项目名称：	Transit Priority
摘要：	The research teams will explore how to lure travelers from their cars by improving transit operations. The team's initial focus will be conditional signal priority, where green lights are granted to buses arriving at signalized intersections as needed. The University of South Florida (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 transit signal priority (TSP) efforts to understand the impact of the pandemic on these strategies. The University of California, Berkeley (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. The research teams will develop/refine an analytical model that uses real-time measurements of bus headways to nudge late-running buses back on schedule whenever needed.
状态：	Active
资金：	132300
资助组织：	Office of the Assistant Secretary for Research and Technology
项目负责人：	Li, Xiaopeng
执行机构：	National Institute for Congestion Reduction<==>University of California Berkeley
开始时间：	20200831
预计完成日期：	20210930
主题领域：	Operations and Traffic Management；Planning and Forecasting；Public Transportation