Traffic Queue Prediction and Warning System
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2019-08-01
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Edition:Final Report Oct 1, 2015 -June 30, 2019
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Abstract:The purpose of the study was to explore and develop analytical models and field-data based algorithms for the identification or detection of the “end-of-queue” (EOQ) in freeway incident or congestion situations. TDOT’s real-time ITS traffic data was the primary basis for this study, so the algorithms could be verified in the field and deployed in the future using the same database. The crowdsourced WAZE database was also included in this study to afford TDOT the flexibility of managing incident cases outside of the urban TMC/RDS data coverage areas. The study reviewed the state-of-the practice of real-time queue length and end-of-queue identification methodologies, evaluated the suitability of real-time traffic data sources as potential input, and developed queue detection and prediction algorithms based on these data sources. The study also assessed the risks associated with the end-of queue crash prediction model and evaluated means for warning the public of a non-recurring/unexpected queue situation, particularly outside of the major urban areas. Based on the means for warning the public, some implementation strategies were identified. This study developed a technical framework that can detect and predict the EOQ location, which is a crucial step towards protecting the queue. The system developed in this study dynamically detects the EOQ and predicts its movement in spatiotemporal domains based on real-time traffic data using traffic flow models. One of the biggest benefits of this endeavor is crash prevention. The system can proactively manage the queue, with focus on non-recurrent events, and reduce rear-end collision risks. Timely dissemination of the EOQ information could effectively slow down approaching drivers, divert drivers further upstream, and, hence, reduce the safety hazards of non-recurring events. Potential implementation strategies for warning the motoring public include infrastructure-based devices, such as changeable message signs, and vehicle-based mechanisms, such as WAZE type navigation apps and DSRC/5G communications. In the era of connected and automated vehicles (CAV), more effective EOQ detection and warning systems could become a standard feature for cars as well as freeway operation centers.
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