WCSE

Traffic Anomaly Classification by Support Vector Machine with Radial Basis Function on Chula-SSS Urban Road Network

Abstract— Recognition of urban road traffic pattern is an important part of intelligent transportation systems . An enormous number of traffic data could be obtained with the development of information techniques . This motivates the application of machine learning in the road traffic area, especially in traffic incident detection. Incident detection algorithm in the machine learning can be defined as a binary classification problem, where each occurrence is the traffic state on a road segment at a particular time. This paper is concerned with how to detect traffic anomaly patterns in an urban road network by using potential sensor data. In this paper, by using Simulation of Urban Mobility (SUMO) software, we have chosen to work on the Chula-Sathorn SUMO Simulator (Chula -SSS) dataset. SUMO enables users to simulate traffic networks and supports the traffic data by setting up conveniently simulated lane area detectors. By using calibrated Chula-SSS dataset, anomaly traffic patterns have been generated and classified with the support vector machine algorithm with the radial basis function. The algorithm has been shown here to detect accurately of at least 87% (and 71 %) of the simulated lane-closure incidences, by relying on sensors from (i) within the incident area, and (ii) at the upstream as well as downstream areas adjacent to that incident link, respectively.

Index Terms— Incident Detection, Intelligent Transportation System, Simulation of Urban MObility (SUMO), Support Vector Machine

Hideya Ochiai
Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, JAPAN
Ei Ei Mon, Chaiyachet Saivichit, Chaodit Aswakul
Wireless Network and Future Internet Research Unit, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, THAILAND

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Cite: Ei Ei Mon, Hideya Ochiai, Chaiyachet Saivichit, Chaodit Aswakul, "Traffic Anomaly Classification by Support Vector Machine with Radial Basis Function on Chula-SSS Urban Road Network," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering WCSE_2019_SPRING, pp. 73-80, Yangon, Myanmar, February 27-March 1, 2019.