@article{ref1,
title="Lane-based queue length estimation at signalized intersections using single-section license plate recognition data",
journal="Transportmetrica B: transport dynamics",
year="2022",
author="Tang, Keshuang and Wu, Hao and Yao, Jiarong and Tan, Chaopeng and Ji, Yangbeibei",
volume="10",
number="1",
pages="293-311",
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.<p /> <p>Language: en</p>",
language="en",
issn="2168-0566",
doi="10.1080/21680566.2021.1991504",
url="http://dx.doi.org/10.1080/21680566.2021.1991504"
}