Traffic Condition Detection in Freeway by using Autocorrelation of Density and Flow

Document Type : Research Paper


1 Ph.D. Candidate, Electrical Engineering Department, Shahid Beheshti University

2 Associate Professor, Electrical Engineering Department Shahid Beheshti University


Traffic conditions vary over time, and therefore, traffic behavior should be modeled as a stochastic process. In this study, a probabilistic approach utilizing Autocorrelation is proposed to model the stochastic variation of traffic conditions, and subsequently, predict the traffic conditions. Using autocorrelation of the time series samples of density and flow which are collected from segments with predefined specifications is the main technique to detect the trend in flow and density changes if exist. A table of possibilities for flow and density changes in two sequential segments will help to detect congestion or any other abnormal traffic events.
In this study proposes a stochastic approach to predict the traffic situation in freeway. The dynamic changes of freeway traffic conditions are addressed with state transition probabilities. For sequence trends of density and flow change, using autocorrelation of speed and flow series will estimate the most likely sequence of traffic states. This is the novelty in this paper that introduces a robust method to recognize the traffic state in a segmented freeway. According to the model definitions 3-state traffic pattern prediction implemented as No Risk (NR), Risk (R) and High risk (HR). We evaluated the proposed method using different data sources of real traffic scenes from Tehran-Qom freeway, Iran. A total of 480 minutes, which corresponds to interstate highways, are chosen for testing. The number of passed vehicle and mean speed are collected by six traffic counter every 1 minute. The estimation rate of this model is 95% over a short time period for the month of July 2014.


-Abdulhai, B. and Ritchie, S. G. (1999) “Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network”, Transportation Research Part C 7, pp. 261–280.
-Adeli, H. and Karim, A. (2000) “Fuzzy-wavelet RBFNN model for freeway incident detection”, Journal of Transportation Engineering Vol. 126, No. 6, pp. 464–471.
-Ahmed, S. A. (1983) “Stochastic processes in freeway traffic”, Traffic Engineering Control, pp.306–310.
-Aultman-Hall, L., Hall, F.L., Shi, Y. and Lyall, B. (1991) “A catastrophe theory approach to freeway incident detection”, Proceedings of the Second International Conference on Applications of Advanced Technologies in Transportation
Engineering, The American Society of Civil Engineers, New York, NY, pp. 373–377.
-Chang, E. C.-P. and Wang, S.-H. (1995) “Improved freeway incident detection using fuzzy set theory”, Transportation Research Record Vol. 1453, 75–82.
-Cook, A. R. and Cleveland, D. E. (1974) “Detection of freeway capacity-reducing incidents by traffic-stream measurements”, Transportation Research Record, Vol. 495, pp.1–11.
-Daganzo, C. (1995) “The cell transmission model, Part II: Network traffic” Transportation Research,Part B, Vol. 29, No. 2, pp.79–93.
-Dudek, C. L. and Messer, C. J. (1974) “Incident detection on urban freeways” Transportation Research Record, Vol.495, pp. 12–24.
-Golob T. F., Will, Rocker and Yannis, Pavlis (2008) “ Probabilistic models of freeway safety performance using traffic flow data as predictors”, Safety Science, Vol.46 (2008) pp.1306-1333
-Hoogendoorn, S. P. and Bovy, P. H. L. (2001) “State of the art of vehicular traffic flow modelling“, Jornal of Systems and Control engineering, Vol.215(4)
-Hsiao, C.-H., Lin, C.-T. and Cassidy, M. (1994) “Application of fuzzy logic and neural networks to automatically detect freeway traffic incidents” Journal of Transportation Engineering Vol.120 (5), pp.753–772.
-Ishak, S. and Al-Deek, H. (1999) “Performance of automatic ANN-based incident detection on freeways” Journal of Transportation Engineering, pp.281–290.
-Kerner, B. S. (2013) “Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory”, A brief review Physic A: Statistical Mechanics and its Applications Vol. 392 (21), pp. 5261-5282.
-Kurzhanskiy, A. and Varaiyav, P. (2010) “Active traffic management on road networks: a macroscopic approach“. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 368(1928), pp. 4607-4626
-Levin, M. and Krause, G. M. (1978) “Incident detection: a Bayesian approach” Transportation Research Record Vol.682, pp.52–58.
-Lin, W.-H. (1995) “Incident detection with data from loop surveillance systems: the role of wave analysis”, Dissertation, Institute of Transportation Studies, University of California at Berkeley.
-Payne, H. J. and Tignor, S. C. (1978) “Freeway incident detection algorithms based on decision trees with states”, Transportation Research Record, Vol. 682, pp.30–37.
-Porikli, F. and Li, X. (2004) “Traffic congestion estimation using HMM models without vehicle tracking”, Mitsubishi Electronic Reserch Labratories
-Qiu, T.Z., Lu, X., Chow, A. H. F. and Shladover, S. E. (2010) “Estimation of freeway traffic density with loop detector and prob vehicle data“, Transportation Research Record, Jornal of Transportation Research Board, No. 2178, pp. 21-29.
-Stephanedes, Y. J. and Chassiakos, A. P. (1993) “Application of filtering techniques for incident detection”, Journal of Transportation Engineering, Vol. 119, No. 1, pp.13–26.
-Torfehnejad, H. (2011) “A practical dynamic speed limit control method using real-time traffic counting systems“, 18th ITS World Congress, 16-20 October, Orlando Florida, USA
-Torfehnejad, H. and Adamnejad, Sh. (2014) “A practical symple technique to detect abnormal traffic flow in freeway“, 21th ITS World Congress, 7-11September, Detroit, USA
-U.S. Department of Transportation. Federal Highway Administration (2006) “Traffic detector handbook“, Third edition- Volume 1
-Whitson, R. H., Burr, J. H., Drew, D. R. and McCasland, W. R. (1969) “Real-time evaluation of freeway quality of traffic service” Highway Research Record, Vol. 289, pp.38–50.
-Willsky A. S., Chow E.Y.,. Gershwin, S. B, Greene, C. S., Houpt, P. K. and Kurkjian, A. L. (1980) “Dynamic model-based techniques for the detection of incidents on freeways“ , IEEE Transactions on automatic control, Vol. AC-25, No.3
-Yang, L. and Sahli, H. [n.d.]“Motion-based traffic analysis and incident detection“ , IBBT/VUB-ETRO, FLEXYS