Lane-based queue length estimation at signalized intersections using single-section license plate recognition data. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Tang, Keshuang; Wu, Hao; Yao, Jiarong; Tan, Chaopeng; Ji, Yangbeibei
• Is Part Of: Transportmetrica. Volume 10:Issue 1(2022)
• Page Start: 293
• Page End: 311
• Publication Date: 2022-12-31
• Subjects: Signalized intersection -- queue length -- license plate recognition data -- miss detection -- E-Divisive with medians; Transportation -- Mathematical models -- Periodicals; 388.015118
• DOI: 10.1080/21680566.2021.1991504
• Languages: English
• ISSNs: 2168-0566

Abstract

Abstract : Due to full record of discharging vehicle headway, License Plate Recognition (LPR) is used as an ideal source in most existing queue length estimation methods through a double-section detection using shock-wave models or input-output models. However, the impacts of heavy vehicles and miss detection by LPR detectors are mostly ignored. Therefore, this paper proposes a lane-based queue length estimation method using single-section LPR detection, considering miss detection and heavy vehicles. The queue length estimation problem is transformed to a change-point identification problem for discharging headways time-series, using E-Divisive with Medians (EDM) method. The maximal queue length is identified as the change-point of the discharging headways with the maximal differences between queued and non-queued vehicles, considering the queuing homogeneity of a lane group and the miss detection rate of LPR. The proposed method is validated using simulation and empirical cases with promising performance and good robustness under various conditions.