Classification of Road Traffic Congestion Levels From GPS Data Using a Decision Tree Algorithm and Sliding Windows

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Provided by: International Association of Engineers
Topic: Mobility
Format: PDF
The authors proposed a technique to identify road traffic congestion levels from velocity of mobile sensors with high accuracy and consistent with motorists' judgments. The data collection utilized a GPS device, a webcam, and an opinion survey. Human perceptions were used to rate the traffic congestion levels into three levels: light, heavy, and jam. Then the ratings and velocity were fed into a decision tree learning model (J48). They successfully extracted vehicle movement patterns to feed into the learning model using a sliding windows technique.
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