Automatic Traffic Queue-End Identification using Location-Based Waze User Reports
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.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
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Supplemental Notes:
- The Waze data and roadside sensor data that support the findings of this study are available from Tennessee Department of Transportation (TDOT). Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the authors with the permission of TDOT. © National Academy of Sciences: Transportation Research Board 2021.
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Authors:
- Liu, Yuandong
- Zhang, Zhihua
- Han, Lee D
- Brakewood, Candace
- Publication Date: 2021-10
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 895-906
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume: 2675
- Issue Number: 10
- Publisher: Sage Publications, Incorporated
- ISSN: 0361-1981
- EISSN: 2169-4052
- Serial URL: http://journals.sagepub.com/home/trr
Subject/Index Terms
- TRT Terms: Crowdsourcing; Detection and identification technologies; Location data; Sensors; Traffic congestion; Traffic queuing
- Identifier Terms: Waze
- Geographic Terms: Knoxville (Tennessee)
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01776758
- Record Type: Publication
- Files: TRIS, TRB, ATRI
- Created Date: Jul 21 2021 4:51PM