Real-Time Short-Term Forecasting Based on Information Management

Jamal Raiyn, Tomer Toledo

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

DOI: 10.4236/jtts.2014.41002                

Abstract

Traffic congestions and road accidents continue to increase in industry countries. There are three basic strategies to relieve congestion. The first strategy is to increase the transportation infrastructure. However, this strategy is very expensive and can only be accomplished in the long-term. The second strategy is to limit the traffic demand or make traveling more expensive that will be strongly opposed by travelers. The third strategy is to focus on efficient and intelligent utilization of the existing transportation infrastructures. This strategy is gaining more and more attention because it’s well. Currently, the Intelligent Transportation System (ITS) is the most promising approach to implementing the third strategy. Various forecast schemes have been proposed to manage the traffic data. Many studies showed that the moving average schemes offered meaningful results compared to different forecast schemes. This paper considered the moving average schemes, namely, simple moving average, weighted moving average, and exponential moving average. Furthermore, the performance analysis of the shortterm forecast schemes will be discussed. Moreover, the real-time forecast model will consider the abnormal condition detection.

Keywords

Forecast Scheme; Moving Average; Intelligent Transportation System

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. P. Bickel, C. Chen, J. Kwon, J. Rice, V. E. Zwet and P. Varaiya, “Measuring Traffic,” Statistical Science, Vol. 22, No. 4, 2007, pp. 581-597. http://dx.doi.org/10.1214/07-STS238
[2]	T. M. Borzacchielo, “The Use of Data from Mobile Phone Networks for Transportation Applications,” Transportation Research Board, 2010, 20 p.
[3]	R. Chrobok, O. Kaumann, J. Wahle and M. Scheckenberg, “Three Categories of Traffic Data: Historical, Current, and Predictive,” 9th IFAC Symposium, Braunschweig, Vol. 1, 13-15 June 2000, pp. 221-226.
[4]	R. Chrobok, O. Kaumann, J. Wahle and M. Schreckenberg, “Different Methods of Traffic Forecast Based on Real Data,” European Journal of Operational Research, Vol. 15, No. 3, 2004, pp. 558-568. http://dx.doi.org/10.1016/j.ejor.2003.08.005
[5]	M. Alger, “Real-Time Traffic Monitoring Using Mobile Phone Data,” Proceedings on 49th European Study, 2004.
[6]	D. Wild, “Short-Term Forecasting Based on a Transformation and Classification of Traffic Volume Time Series,” International Journal of Forecasting, Vol. 13. No. 1, 1997, pp. 63-72. http://dx.doi.org/10.1016/S0169-2070(96)00701-7
[7]	Y. Lv and S. Tang, “Real-Time Highway Traffic Accident Prediction Based on the k-Nearest Neighbor Method,” International Conference on Measuring Technology and Mechatronics Automation, Vol. 3, 2010, pp. 547-550.
[8]	Y. Lee and C. H. Wei, “A Computerized Feature Selection Using Genetic Algorithms to Forecast Freeway Accident Duration Times,” Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 2, 2010, pp. 132-148. http://dx.doi.org/10.1111/j.1467-8667.2009.00626.x
[9]	M. Martínez-Zarzuela, “Wavelet-Based Denoising for Traffic Volume Time Series Forecasting with Self-Organizing Neural Networks,” Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 7, 2010, p. 530-545.
[10]	H. Nicholson and C. D. Swann, “The Prediction of Traffic Flow Volumes Based on Spectral Analysis,” Transportation Research, Vol. 8, No. 6, 1974, pp. 533-538. http://dx.doi.org/10.1016/0041-1647(74)90030-6
[11]	I. Okutani and J. Y. Stephanedes, “Dynamic Prediction of Traffic Volume through Kalman Filtering Theory,” Transportation Research, Vol. 18B, No. 1, 1984, pp. 1-11. http://dx.doi.org/10.1016/0191-2615(84)90002-X
[12]	B. Ronen, A. Coman and E. Schragenheim, “Peak Management,” International Journal of Production Research, Vol. 39, No. 14, 2011, pp. 3183-3193. http://dx.doi.org/10.1080/00207540110054588
[13]	M. Sabry, H. Abd-El-Latif, S. Yousef and N. Badra, “A Time-Series Forecasting of Average Daily Traffic Volume,” Australian Journal of Basic and Applied Sciences, Vol. 1, No. 4, 2007, pp. 386-394.
[14]	M. Sabry, H. Abd-El-Latif and N. Badra, “Comparison between Regression and Arima Models in Forecasting Traffic Volume,” Australian Journal of Basic and Applied Sciences, Vol. 1, No. 2, 2007, pp. 126-136.
[15]	A. Stathopoulos, L. Dimitriou and T. Tsekeris, “Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow,” Computer-Aided Civil and Infrastructure Engineering, Vol. 23, No. 7, 2008, pp. 521-535. http://dx.doi.org/10.1111/j.1467-8667.2008.00558.x
[16]	Y. J. Stephanedes, P. G. Michalopoulos and R. A. Plum, “Improved Estimation of Traffic Flow for Real-Time Control,” Transportation Research Record, Vol. 795, 1981, pp. 28-39.
[17]	H. Tu, H. Van Lint and H. Van Zuylen, “The Effects of Traffic Accidents on Travel Time Reliability,” IEEE Conference on Intelligent Transportation Systems, Beijing, 12-15 October 2008, pp. 79-84.
[18]	E. I. Vlahogianni, M. G. Karlaftis and J. C. Golias, “Temporal Evolution of Short-Term Urban Traffic Flow: A Non-Linear Dynamics Approach,” Computer-Aided Civil and Infrastructure Engineering, Vol. 23, No. 7, 2008, pp. 536-548.
[19]	Z. Wang and P. Murray-Tuite, “Modeling Incident-Related Traffic and Estimating Travel Time with a Cellular Automaton Model,” Transportation Research Board, 2010, 21 p.
[20]	M. S. Ahmed and A. R. Cook, “Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques,” Transportation Research Record, Vol. 722, 1997, pp. 1-9.
[21]	J. Andrada-Felix and F. Fernandez-Rodriguez, “Improving Moving Average Trading Rules with Boosting and Statistical Learning Methods,” Journal of Forecasting, Vol. 27, No. 5, 2008, pp. 433-449. http://dx.doi.org/10.1002/for.1068
[22]	R. R. Andrawis and F. A. Atiya, “A New Bayesian Formulation for Holt’s Exponential Smoothing,” Journal of Forecasting, Vol. 28, No. 3, 2009, pp. 218-234. http://dx.doi.org/10.1002/for.1094
[23]	A. Guin, “Travel Time Prediction using a Seasonal Autoregressive Integrated Moving Average Time Series Model,” Proceedings of the IEEE Intelligent Transportation Systems Conference, Toronto, 17-20 September 2006, pp. 493-498.
[24]	H. Jo, B. Lee, Y.-C. Na, H. Lee and B. Oh, “Prioritized Traffic Information Delivery Based on Historical Data Analysis,” Proceedings of the 2007 IEEE Intelligence Transportation Systems Conference, Seattle, 30 September-3 October 2007, pp. 568-573. http://dx.doi.org/10.1109/ITSC.2007.4357758
[25]	A. Karim and H. Adeli, “Fast Automatic Incident Detection on Urban and Rural Freeways Using Wavelet Energy Algorithm,” Journal of Transportation Engineering, ASCE, Vol. 129, No. 1, 2003, pp. 57-68. http://dx.doi.org/10.1061/(ASCE)0733-947X(2003)129:1(57)
[26]	H. Lee, N. K. Chowdhury and J. Chang, “A New Travel Time Prediction Method for Intelligent Transportation Systems,” Springer-Verlag, Berlin, 2008, pp. 473-483.
[27]	J. Xia, “Predicting Freeway Travel Time Under Incident Condition,” Transportation Research Record: Journal of the Transportation Research Board, 2010, pp. 58-66.
[28]	X. Q. Zhao, R. M. Li and X. X. Yu, “Incident Duration Model on Urban Freeways Based on Classification and Regression Tree,” 2nd International Conference on Intelligent Computation Technology and Automation, Changsha, 10-11 October 2009, pp. 625-628.
[29]	X. Zheng and M. Liu, “An Overview of Accident Forecasting Methodologies,” Journal of Loss Prevention in the Process Industries, Vol. 22, No. 4, 2009, pp. 484-491. http://dx.doi.org/10.1016/j.jlp.2009.03.005
[30]	J. Han, and M. Kamber, “Data Mining: Concepts and Techniques,” 2nd Edition, Morgan Kaufmann, San Francisco, 2006.