Lane-based queue length estimation at signalized intersections using single-section license plate recognition data

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.

KEYWORDS:

• Signalized intersection
• queue length
• license plate recognition data
• miss detection
• E-Divisive with medians

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the Natural Science Foundation of China [grant number 61673302], the Shanghai Municipal Science and Technology Major Project [grant number 2021SHZDZX0100], the Fundamental Research Funds for the Central Universities, and the Shanghai Science and Technology Commission Fund Project [grant number 19DZ1208800, 20DZ1202703, 21DZ1203803].