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.
References
[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. http://dx.doi.org/10.1016/S0169-2070(96)00701-7
[4]
Zheng, X. and Liu, M. (2009) An Overview of Accident Forecasting Methodologies. Journal of Loss Prevention in the Process Industries, 22, 484-491. http://dx.doi.org/10.1016/j.jlp.2009.03.005
[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. http://dx.doi.org/10.1002/for.1068
[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. http://dx.doi.org/10.1061/(ASCE)0733-947X(2003)129:1(57)
[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.
