Real-Time Road Traffic Anomaly Detection

Jamal Raiyn, Tomer Toledo

Faculty of Civil and Environmental Engineering, Department of Transportation Engineering, Technion, Haifa, Israel.
Faculty of Exact Science, Computer Science Department, Al Qasemi Acedemic College, Baqa Al Qarbiah, Israel.

DOI: 10.4236/jtts.2014.43023                

Abstract

Many modeling approaches have been proposed to help forecast and detect incidents. Accident has received the most attention from researchers due to its impacts economically. The traffic congestion costs billions of dollars to economy. The main reasons of major percentage of traffic congestion are the incidents. Road accidents continue to increase in digital age. There are many reasons for road accidents. This paper will discuss and introduce new algorithm for road accident detection. Various forecast schemes have been proposed to manage the traffic data. In this paper we will introduce road accident detection scheme based on improved exponential moving average. The proposed traffic incident detection algorithm is based on the automatic exponential moving average scheme. The detection algorithm is based on analyzing the collected traffic flow parameters. The detection algorithm is based on analyzing the collected traffic flow parameters. In addition a real-time accident forecast model was developed based on short-term variation of traffic flow characteristics.

Keywords

Anomaly Traffic, Detection Scheme, Moving Average, Intelligent Transportation System

Conflicts of Interest

The authors declare no conflicts of interest.

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