Real-Time Road Traffic Anomaly Detection

DOI: 10.4236/jtts.2014.43023   PDF   HTML   XML   4,121 Downloads   5,719 Views   Citations


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.

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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.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Conference on Intelligent Transportation Systems, Beijing, 12-15 October 2008.
[2] Wang, Z. and Murray-Tuite, P. (2010) Modeling Incident-Related Traffic and Estimating Travel Time with a Cellular Automaton Model. Proceedings of Transportation Research Board’s 89th Annual Meeting CD-ROM, 10-14 January 2010, Washington, DC.
[3] Wild, D. (1997) Short-Term Forecasting Based on a Transformation and Classification of Traffic Volume Time Series. International Journal of Forecasting, 13, 63-72.
[4] Zheng, X. and Liu, M. (2009) An Overview of Accident Forecasting Methodologies. Journal of Loss Prevention in the Process Industries, 22, 484-491.
[5] Andrada-Felix, J. and Fernandez-Rodriguez, F. (2008) Improving Moving Average Trading Rules with Boosting and Statistical Learning Methods. Journal of Forecasting, 27, 433-449.
[6] Guin, A. (2006) Travel Time Prediction Using a Seasonal Autoregressive Integrated Moving Average Time Series Mode. Proceedings of the IEEE Intelligent Transportation Systems Conference, Toronto, 17-20 September 2006, 493-498.
[7] Lv, Y. and Tang, S. (2010) Real-time Highway Traffic Accident Prediction Based on the K-Nearest Neighbor Method. International Conference on Measuring Technology and Mechatronics Automation, Volume 3, 547-550.
[8] Xia, J (2010) Predicting Freeway Travel Time under Incident Condition. Proceedings of Transportation Research Board’s 89th Annual Meeting CD-ROM, 10-14 January 2010, Washington, DC.
[9] Alger, M. (2004) Real-Time Traffic Monitoring Using Mobile Phone Data. Proceedings of 49th European Study European Study Group with Industry, Oxford, United Kingdom.
[10] Stephanedes, Y.J., Michalopoulos, P.G. and Plum, R.A. (1981) Improved Estimation of Traffic Flow for Real-Time Control. Transportation Research Record, 795, 28-39.
[11] Jo, H., Lee, B., Na, Y.-C., Lee, H. and Oh, B. (2007) Prioritized Traffic Information Delivery Based on Historical Data Analysis. Proceedings of the 2007 IEEE Intelligence Transportation Systems Conference, Seattle, September 30-October 3 2007, 568-573.
[12] Karim, A. and Adeli, H. (2003) Fast Automatic Incident Detection on Urban and Rural Freeways Using Wavelet Energy Algorithm. Journal of Transportation Engineering, 129, 57-68.
[13] Lee, H., Chowdhury, K.N. and Chang J. (2008) A New Travel Time Prediction Method for Intelligent Transportation Systems. Springer-Verlag, Berlin, 473-483.
[14] Xiaoqiang, Z., Ruimin, L. and Xinxin, Y. (2010) Incident Duration Model on Urban Freeways Based on Classification and Regression Tree. 2nd International Conference on Intelligent Computation Technology and Automation, TRB 2010 Annual Meeting, 2, 526-528.

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