Queue Length Estimation for Freeway Facilities: Based on Combination of Point Traffic Detector and Automatic Vehicle Identification Data. Issue 1 (2017)

• Record Type: Journal Article
• Authors: Fakharian Qom, Somaye; Hadi, Mohammed; Xiao, Yan; Al-Deek, Haitham
• Is Part Of: Transportation research record. Volume 2616:Issue 1(2017)
• Page Start: 19
• Page End: 26
• Publication Date: 2017
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.3141/2616-03
• Languages: English
• ISSNs: 0361-1981

Abstract

Queue length is a critical performance measure for assessing and managing transportation network performance. Two new methods that integrate data from point traffic detectors and automatic vehicle identification (AVI) readers to estimate the queue length of freeway segments for both off-line and real-time applications are developed in this study. One method estimates the queue length between two detectors by using linear interpolation between the travel time measurement based on AVI data when the link is fully queued and when no queue is present. In the second method, a segment with a partial queue is divided into two subsegments: the first is assumed to be similar to upstream traffic conditions and the second to downstream traffic conditions. Then, the length of each part is calculated from AVI speed data. The performance of these methods is assessed and compared in two case studies that are based on simulation data and real-world data. The results show that using a combination of point detector data and AVI data produces accurate estimates of queue length. The queue estimation method based on cumulative volumes collected with point detectors alone also produces reasonably good estimates but requires additional ramp detection and assumptions regarding moving queue density. The two combination methods produce results that are close to each other based on simulation data and real-world data. The segmentation method produces better results based on real-world data.