Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. The proposed method was tested with 34 traffic congestion cases in the Knoxville,Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Automatic Traffic Queue-End Identification using Location-Based Waze User Reports


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2021-07-07




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Secondary Crash Identification using Crowdsourced Waze User Reports

    Zhang, Zhihua / Liu, Yuandong / Han, Lee D. et al. | Transportation Research Record | 2021


    Exploration and evaluation of crowdsourced probe-based Waze traffic speed

    Zhang, Zhihua / Han, Lee D. / Liu, Yuandong | Taylor & Francis Verlag | 2022


    Evaluating the Reliability, Coverage, and Added Value of Crowdsourced Traffic Incident Reports from Waze

    Amin-Naseri, Mostafa / Chakraborty, Pranamesh / Sharma, Anuj et al. | Transportation Research Record | 2018


    Are You Gonna Go My WAZE?

    Michael Pack | Online Contents | 2017


    Evaluating the Coverage and Spatiotemporal Accuracy of Crowdsourced Reports Over Time: A Case Study of Waze Event Reports in Tennessee

    Liu, Yuandong / Hoseinzadeh, Nima / Gu, Yangsong et al. | Transportation Research Record | 2023