Abstract
Transit signal priority (TSP) is a promising low-cost strategy that gives preferential treatments for the buses to go through intersections with minimum delay time. In this paper, a new TSP control model was presented for isolated intersections to minimize bus delay and to reduce the impact of TSP on other vehicles by optimizing signal control phase selection and compression. This paper starts with the phase selection and compression strategies to provide treatments to bus priority requests. Then, two new features on phase selection and compression aspects are applied to TSP, i.e. the time that a bus priority request needs is provided by the phase(s) with the lowest traffic volume, and multi-phases can be selected to serve a bus request. Field data are collected from a major traffic corridor in Changzhou (China) and applied for VISSIM simulation. The proposed TSP control model as well as the fixed-time control and the conventional TSP control models are tested and compared under different traffic demands, headways and maximum saturation degrees. The comparative results showed that the proposed model outperformed the conventional TSP control model in terms of reducing bus delay, minimizing the impact on other vehicles and reducing the stop rate for buses. This paper reveals that, the proposed TSP strategy can significantly optimize the phase compression process and improve transit efficiency.
Highlights
To address the increasing traffic congestion in urban areas, both researchers and government officers are seeking for effective yet inexpensive solutions that do not involve new infrastructure construction to enhance transportation system performance
Active Transit Signal Priority (TSP) techniques rely on detecting transit vehicles arrival time at an intersection and adjusting the signal timing dynamically to reduce the bus delay (Janos, Furth 2002; Wahlstedt 2011; Li, Zhang 2012; Ma et al 2013; Ahmed 2014; Wang et al 2014; Zeng et al 2014; Wolput et al 2015).TSP operating in real-time shares the same TSP strategies such as early green, green extension and phase insertion with active priority, and provides priority while simultaneously optimizing traffic performance (Balke et al 2000; Duerr 2000; Zhou, Gan 2009; Christofa, Skabardonis 2011; Hu et al 2014, 2015)
Note that when headway and v/c ratio are set to 502 s and 0.62, the percentage of buses passing through the intersection without a stop are 100% with proposed TSP control model, and 0% with the conventional TSP control model
Summary
Active TSP techniques rely on detecting transit vehicles arrival time at an intersection and adjusting the signal timing dynamically to reduce the bus delay (Janos, Furth 2002; Wahlstedt 2011; Li, Zhang 2012; Ma et al 2013; Ahmed 2014; Wang et al 2014; Zeng et al 2014; Wolput et al 2015).TSP operating in real-time shares the same TSP strategies such as early green, green extension and phase insertion with active priority, and provides priority while simultaneously optimizing traffic performance (Balke et al 2000; Duerr 2000; Zhou, Gan 2009; Christofa, Skabardonis 2011; Hu et al 2014, 2015).
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