Journal of Transportation Technologies

Volume 4, Issue 3 (July 2014)

ISSN Print: 2160-0473   ISSN Online: 2160-0481

Google-based Impact Factor: 1.62  Citations  h5-index & Ranking

Real-Time Road Traffic Anomaly Detection

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DOI: 10.4236/jtts.2014.43023    5,320 Downloads   8,186 Views  Citations

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.

Share and Cite:

Raiyn, J. and Toledo, T. (2014) Real-Time Road Traffic Anomaly Detection. Journal of Transportation Technologies, 4, 256-266. doi: 10.4236/jtts.2014.43023.

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