Transit Signal Priority (TSP) strategies have been widely applied to reduce bus travel delay and increase bus service reliability. Conventional TSP strategies aim to help buses cross intersections without stopping, either by extending the current green or truncating the current red upon the bus approach. The state-of-the-art TSP strategies enable dynamic (and optimal) TSP plans to reflect real-time traffic conditions. Among all the existing adaptive TSP strategies, it is typical to use a performance index (PI), a weighted summation of all delay (buses and general vehicles), to evaluate each candidate TSP plan and the weights of the PI components reflecting the corresponding importance. The performance of TSP optimization depends on three factors: delay (buses and general vehicles) estimation, weights determination and optimization formulation. There are key academic contributions of this paper: 1. developing an accurate bus delay estimation model; 2. developing a mechanism to dynamically adjust the weights of the PI components to reflect the changing necessity of TSP under different conditions; 3. formulating the TSP optimization into a mathematical programming problem and obtaining the optimal TSP strategies scientifically. In addition, we also developed an adaptive TSP simulation platform using the full-scale signal simulator, ASC/3, in VISSIM. The optimal TSP plans are activated and cancelled through overriding and recovering prevailing signal timings. Lastly, through a case study in VISSIM, it was found that, compared with conventional active TSP strategies, the new adaptive TSP strategy could further reduce bus travel time, while maintaining a better balance of service on non-TSP approaches along a 7.4 kilometer bus corridor in Edmonton, Alberta.
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