@article{ref1,
title="Real-time queue length estimation using event-based advance detector data",
journal="Journal of intelligent transportation systems: technology, planning, and operations",
year="2018",
author="An, Chengchuan and Wu, Yao-Jan and Xia, Jingxin and Huang, Wei",
volume="22",
number="4",
pages="277-290",
abstract="Real-time queue length information at signalized intersections is useful for both performance evaluation and signal optimization. Previous studies have successfully examined the use of high-resolution event-based data to estimate real-time queue lengths. Based on the identification of critical breakpoints, real-time queue lengths can be estimated by applying the commonly used shockwave model. Although breakpoints can be accurately identified using lane-by-lane detection, few studies have investigated queue length estimation using single-channel detection, which is a common detection scheme for actuated signal control. In this study, a breakpoint misidentification checking process and two input-output models (upstream-based and local-based) are proposed to address the overestimation and short queue length estimation problems of breakpoint-based models. These procedures are integrated with a typical breakpoint-based model framework and queue-over-detector identification process. The proposed framework was evaluated using field-collected event-based data along Speedway Boulevard in Tucson, Arizona. Significant improvements in maximum queue length estimates were achieved using the proposed method compared to the breakpoint-based model, with mean absolute errors of 35.7 and 105.6 ft., respectively.<p /> <p>Language: en</p>",
language="en",
issn="1547-2450",
doi="10.1080/15472450.2017.1299011",
url="http://dx.doi.org/10.1080/15472450.2017.1299011"
}