Real-Time Traffic Congestion Detection with Vehicle Moving Patterns
Date Issued
2015
Date
2015
Author(s)
Wang, Chenqi
Abstract
Traffic congestion in urban areas is a severe problem in many cities around the world. Existing solutions require data from a considerably large number of vehicles on the same road to accurately detect traffic congestion of a particular road. We notice that the behaviors of vehicles in congested and noncongested states are discriminable, thus we present a novel approach to detect the traffic states using motion patterns of an individual vehicle in real-time without any other preliminary knowledge. Specifically, only a smart phone is needed to predict the traffic state in our system. The biggest advantage of such an approach is that the system can function properly even if there are only a smaller number of vehicles equipped with the system, which is usually the case at the early stage of the deployment of a vehicle-to-vehicle (V2V) network or a large-scale intelligent transportation system. In our solution, machine learning mechanisms are utilized to classify the traffic state by extracting the motion behaviors of a vehicle. Our model development utilizes highly accurate vehicle traces collected at several real-world intersections with LIDAR. In implementation, we use KNN algorithm to determine the traffic state by comparing the real-world GPS trace with our collected traces. Since the length of traces will be different according to different road segments, we utilize dynamic time warping (DTW) mechanism to calculate the distance. Evaluation results show that, with only data from an individual vehicle, our classification model can achieve a promising 90% precision
Subjects
Congestion Detection
Machine Learning
Vehicular Network
Type
thesis
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