A bi-Level programming method for SPaT estimation at fixed-time controlled intersections using license plate recognition data. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Yao, Jiarong; Wu, Hao; Tang, Keshuang
• Is Part Of: Transportmetrica. Volume 11:Issue 1(2023)
• Page Start: 1045
• Page End: 1070
• Publication Date: 2023-12-31
• Subjects: Fixed-time signalized intersection -- signal phase and timing (SPaT) estimation -- license plate recognition data -- bi-level programming; Transportation -- Mathematical models -- Periodicals; 388.015118
• DOI: 10.1080/21680566.2023.2165191
• Languages: English
• ISSNs: 2168-0566

Abstract

Abstract : Signal phase and timing (SPaT) information is a necessary input for traffic performance evaluation. However, current SPaT estimation studies mainly focus on estimation of cycle length or green time of a certain movement, and are realized mostly by floating car data whose data quality significantly affects the estimation accuracy. As license plate recognition (LPR) systems are becoming a widely implemented and reliable data source in China, in this study, a SPaT estimation method is proposed using the LPR data for fixed-time controlled intersections. The SPaT estimation problem is formulated as a bi-level programming model to find the optimal match between the phase boundaries and the LPR passing time series in the study period. Evaluation is done with an empirical case and compared with an existing method, results show that the estimation accuracies of the phase duration can reach 90.0%, outperforming the existing method and demonstrating great potential for practical application.