Development and comparison of dynamic transit signal priority strategies

Abstract

Transit Signal Priority (TSP) is a popular strategy used to enhance the performance of transit systems by modifying the signal control logic to give transit signal priority through signalized intersections. Conventional TSP strategies used in most cities have been shown to offer significant benefits in minimizing the delays of transit vehicles. However, there have been concerns about some shortcomings of conventional TSP strategies which limited their applications. The main concern is the potential negative impact on the cross street traffic. Another concern is the static nature of conventional TSP strategies and the lack of responsiveness to real-time traffic and transit conditions. This paper describes the development and evaluation of a dynamic Transit Signal Priority control system which has the ability to provide signal priority in response to real-time traffic and transit conditions. The dynamic TSP system consists of three main components: a virtual detection system, a dynamic arrival prediction model, and a dynamic TSP algorithm. The virtual detection system was developed in a WSSIM microsimulation model. Subsequently, several bus arrival prediction models were developed using linear regression and Neuro-fuzzy methods. Techniques such as Kalman and Bayes filters were used to refine the prediction. Finally, a dynamic TSP algorithm was developed to determine what TSP strategy to use and when to apply it. The dynamic TSP system was tested and compared to the conventional TSP system using the microsimulation model. Scenarios with varying simulation parameters and traffic volumes were tested. Results showed that the dynamic TSP system outperformed the conventional one. © 2008 Taylor & Francis Group, London.