Route-Based Transit Signal Priority Using Connected Vehicle Technology to Promote Bus Schedule Adherence
Xiaosi Zeng, Yunlong Zhang, Jian Jiao, Kai Yin
Abstract
In this paper, we explore the use of enriched bus data enabled by the Connected Vehicle (CV) technologies and propose signal timing optimization models that aim to improve bus service reliabilities via Transit Signal Priorities (TSP). Specifically, a route-based TSP model (R-TSP) and its local version (L-TSP) are formulated. Timing and progression deviations are introduced as a simple and novel way to approximate the TSP impacts on passenger vehicle delays. An online adaptive TSP system is developed to leverage the continuous availability of CV data to monitor bus priority needs, trigger new formulations of the TSP models wwithith newly updated bus running data, and implement new signal timings in real-time. Simulation studies are conducted to evaluate a variety of R-TSP and L-TSP model variants to understand their respective effectiveness to improve bus schedule adherence and their impacts to other traffic. Simulation study on a hypothetical corridor show that an R-TSP model variant with both timing and progression deviation defined can improve a 100% late bus fleet to 98.4% of on-time arrival with only 5.5% increase in passenger car delays. In comparison, none of the L-TSP models could produce comparable benefits for buses and caused too much delay on passenger vehicles. These simulation studies conclude that (1) connected vehicle technologies provide critical data to allow route-based TSP model to be formulated simply and solved continuously, and (2) granting bus priority at route level is much more beneficial than only providing bus priority on a signal by signal basis.