Short-Term Traffic Prediction under Both Typical and Atypical Traffic Conditions using a Pattern Transition Model

Traianos-Ioannis Theodorou, Athanasios Salamanis, Dionysios D. Kehagias, Dimitrios Tzovaras, Christos Tjortjis

Abstract

One of the most challenging goals of the modern Intelligent Transportation Systems comprises the accurate and real-time short-term traffic prediction. The achievement of this goal becomes even more critical when the presence of atypical traffic conditions is concerned. In this paper, we propose a novel hybrid technique for short-term traffic prediction under both typical and atypical conditions. An Automatic Incident Detection (AID) algorithm, based on Support Vector Machines (SVM), is utilized to check for the presence of an atypical event (e.g. traffic accident). If such an event occurs, the k-Nearest Neighbors (k-NN) non-parametric regression model is used for traffic prediction. Otherwise, the Autoregressive Integrated Moving Average (ARIMA) parametric model is activated for the same purpose. In order to evaluate the performance of the proposed model, we use open real world traffic data from Caltrans Performance Measurement System (PeMS). We compare the proposed model with the unitary k-NN and ARIMA models, which represent the most commonly used non-parametric and parametric traffic prediction models. Preliminary results show that the proposed model achieves larger accuracy under both typical and atypical traffic conditions.

References

  1. Abdulhai, B., Porwal H., Recker, W., 1997. Short term freeway traffic flow prediction using genetically optimized time delay based neural net-works. Proc., 'th Transportation Research Board Annual Meeting, Washington D. C., USA.
  2. Box, G. E. P. and Jenkins, G. M., 1971. Time series analysis forecasting and control. Operational Research Quarterly, 22(2), June, pp.199-201.
  3. Castro-Neto, M., Jeong, Y. S., Jeong, M. K., Han, L. D., 2009. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, 36(3), pp.6164-6173.
  4. Clark, S., 2003. Traffic Prediction Using Multivariate Nonparametric Regression. Journal of Transportation Engineering, 129(2), pp.161-168.
  5. De Fabritiis, C., Ragona, R., Valenti, G., 2008. Traffic estimation and prediction based on real time floating car data. Proc., IEEE 11th International Conference on Intelligent Transportation Systems, pp.197-203.
  6. Dougherty M. S. and Cobbet M. R., 1997. Short term interurban traffic fore-casts using neural networks. International Journal of Forecasting, 13(1), March, pp.21-31.
  7. Gakis E., Kehagias D., Tzovaras D., 2014. Mining Traffic for Road Incidents Detection. IEEE 17th International Conference on Intelligent Transportation Systems, Qingdao, China, pp. 930-935.
  8. Gao, Y. and Er, M., J., 2005. Narmax time series model prediction: feed-forward and recurrent fuzzy neural network approaches. Fuzzy Sets and Systems, 150(2), March, pp.331-350.
  9. Ghosh, B., Basu, B., O'Mahony, M., 2009. Multivariate short-term traffic flow forecasting using time-series analysis. IEEE Transactions on Intelligent Transportation Systems, 10(2), pp.246-254.
  10. Guo, F., Krishnan, R., Polak, J. W., 2012. Short-Term Traffic Prediction Under Normal and Abnormal Traffic Conditions on Urban Roads. 91st Transportation Research Board Annual Meeting, Washington D. C., USA.
  11. Guo, F., Krishnan, R., Polak, J. W., 2014. A novel threestage framework for short-term travel time prediction under normal and abnormal traffic conditions. 93rd Transportation Research Board Annual Meeting, Washington D. C., USA.
  12. Guo, F., Polak, J. W., Krishnan, R., 2010. Comparison of Modelling Approaches for Short-Term Traffic Prediction under Normal and Abnormal Conditions. IEEE 13th International Conference on Intelligent Transportation Systems, Madeira Island, Portugal.
  13. Guo, J. and Williams, B. M., 2010. Real-time short-term traffic speed level forecasting and uncertainty quantification using layered Kalman filters. Transportation Research Record: Journal of Transportation Research Board, 2175, pp.28-37.
  14. Hu, W., Yan, L., Liu, K., Wang, H., 2015. A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR. Neural Processing Letters, pp.1-18.
  15. Innamaa, S., 2000. Short term prediction of traffic situation using MLP-neural networks. Proc., 7th World Congress on Intelligent Systems, Turin, Italy, pp.1-8.
  16. Kamarianakis, Y. and Prastacos, P., 2005. Space-time modelling of traffic flow. Computers & Geosciences, 31(2), pp.119-133.
  17. Kamarianakis, Y., Shen, W., Wynter, L., 2012. Real-time road traffic forecasting using regime-switching spacetime models and adaptive LASSO. Applied Stochastic Models in Business Industry, 28(4), pp.297-315.
  18. Kindzerske, M. D. and Ni, D., 2007. Composite nearest neighbour nonparametric regression to improve traffic prediction. Transportation Research Record: Journal of Transportation Research Board. 1993(1), pp.30-35.
  19. Min, W. and Wynter, L., 2011. Real-time traffic prediction with spatiotemporal correlations. Transportation Research Part C: Emerging Technologies, 19(4), August, pp.606-616.
  20. Mu, T., Jiang, J., Wang, Y., 2012. Heterogeneous delay embedding for travel time and energy cost prediction via regression analysis. IEEE Transactions on Intelligent Transportation Systems, 14(1), pp. 214-224.
  21. Myung, J., Kim, D. K., Kho, S. Y., Park, C. H., 2012. Travel Time Prediction Using k-Nearest Neighbour Method with Combined Data from Vehicle Detector System and Automatic Toll Collection System. Transportation Research Record: Journal of Transportation Research Board, 2256, pp.51-59.
  22. Ni, M., He, Q., Gao, J., 2014. Using Social Media to Predict Traffic Flow under Special Event Conditions. 93rd Transportation Research Board Annual Meeting, Washington D. C., USA.
  23. Pfeifer, P. E. and Deutsch, S. J., 1980. A three-stage iterative procedure for space-time modelling. Technometrics, 22(1), February, pp.35-47.
  24. Quek, C., Pasqueir, M. and Lim, B. B. S., 2006. Pop-traffic: a novel fuzzy neural approach to road traffic analysis and prediction. IEEE Transactions on Intelligent Transportation Systems, 7(2), June, pp.133-146.
  25. Smith, B. L. and Demetsky, M. J., 1996. Multiple interval freeway traffic flow prediction. Transportation Research Record: Journal of Transportation Research Board, 155(4), pp.136-141.
  26. Stathopoulos, A. and Karlaftis, M. G., 2003. A multivariate state-space approach for urban traffic flow modelling and prediction. Transportation Research Part C: Emerging Technologies, 11(2), April, pp.121-135.
  27. Vlahogianni, E. I., Karlaftis, M. G., Golias, J. C., 2003. A multivariate neural network predictor for short term traffic prediction in urban signalized arterial. Proc. 10th IFAC Symposium on Control in Transportation Systems, Tokyo, Japan, August.
  28. Williams, B. M., Dursavula, P. K., Brown, D. E., 1998. Urban freeway traffic flow prediction - Application of seasonal autoregressive integrated moving average and exponential smoothing models. Transportation Research Record: Journal of Transportation Research Board, 1644, pp.132-141.
  29. Wu, C. H., Wei, C. C., Su, D. C., Chang, M. H., Ho, J. M., 2003. Travel Time Prediction with Support Vector Regression. IEEE 6th International Conference on Intelligent Transportation Systems, Shanghai, China.
  30. Wu, T., Xie, K., Xinpin, D., Song, G., 2012. A online boosting approach for traffic flow forecasting under abnormal conditions. Proc., 9th International Conference on Fuzzy Systems and Knowledge Discovery, Sichuan, China.
  31. Zhang, G. P., 2003. Time series prediction using a hybrid ARIMA and neural network model. Neurocomputing, 50, January, pp.159-175.
  32. Zheng, Z., Su, D., 2014. Short-term traffic volume forecasting: A k-nearest neighbour approach enhanced by constrained linearly sewing principle component algorithm. Transportation Research Part C: Emerging Technologies, 43, pp.143-157.
  33. Wu, Cheng-Ju, Schreiter, Thomas, & Horowitz, Roberto 2014. Multiple-clustering ARMAX-based predictor and its application to freeway traffic flow prediction. American Control Conference (ACC), 2014 IEEE
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Paper Citation


in Harvard Style

Theodorou T., Salamanis A., Kehagias D., Tzovaras D. and Tjortjis C. (2017). Short-Term Traffic Prediction under Both Typical and Atypical Traffic Conditions using a Pattern Transition Model . In Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-242-4, pages 79-89. DOI: 10.5220/0006293400790089


in Bibtex Style

@conference{vehits17,
author={Traianos-Ioannis Theodorou and Athanasios Salamanis and Dionysios D. Kehagias and Dimitrios Tzovaras and Christos Tjortjis},
title={Short-Term Traffic Prediction under Both Typical and Atypical Traffic Conditions using a Pattern Transition Model},
booktitle={Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2017},
pages={79-89},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006293400790089},
isbn={978-989-758-242-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Short-Term Traffic Prediction under Both Typical and Atypical Traffic Conditions using a Pattern Transition Model
SN - 978-989-758-242-4
AU - Theodorou T.
AU - Salamanis A.
AU - Kehagias D.
AU - Tzovaras D.
AU - Tjortjis C.
PY - 2017
SP - 79
EP - 89
DO - 10.5220/0006293400790089