Transit Signal Priority (TSP) strategies are widely used to reduce bus travel delay and improve bus service performance. State-of-the-art TSP strategies help buses cross intersections without stopping, either by green extension or red truncation, and enable adaptive TSP plans that reflect real-time traffic conditions. Among all existing adaptive TSP strategies, there are two types of approaches: 1) objective function-based optimization; 2) logic and (or) rule-based optimization. This thesis develops an adaptive TSP strategy via the objective-function approach. The key contributions include an accurate bus delay estimation model, which implements an adaptive TSP strategy into a programming problem, and an adaptive TSP simulation platform, which uses a full-scale signal emulator, ASC/3, in VISSIM. A case study in VISSIM is conducted to evaluate the proposed adaptive TSP strategy versus conventional TSP strategies. Finally, the proposed TSP is compared with previous studies to investigate advantages and disadvantages.
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