Pattern recognition based speed forecasting methodology for urban traffic network
Abstract
A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.
First Published Online: 4 Sept 2017
Keyword : urban traffic, pattern recognition, short-term forecasting, average speed, artificial neural network
How to Cite
Tettamanti, T., Csikós, A., Kis, K. B., Viharos, Z. J., & Varga, I. (2018). Pattern recognition based speed forecasting methodology for urban traffic network. Transport, 33(4), 959-970. https://doi.org/10.3846/16484142.2017.1352027
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Lo, S.-C. 2013. Expectation-maximization based algorithm for pattern recognition in traffic speed distribution, Mathematical and Computer Modelling 58(1–2): 449–456. https://doi.org/10.1016/j.mcm.2012.11.004
Lozano, A.; Manfredi, G.; Nieddu, L. 2009. An algorithm for the recognition of levels of congestion in road traffic problems, Mathematics and Computers in Simulation 79(6): 1926–1934. https://doi.org/10.1016/j.matcom.2007.06.008
McCulloch, W. S.; Pitts, W. 1943. A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics 5(4): 115–133. https://doi.org/10.1007/BF02478259
Moguerza, J. M.; Muñoz, A. 2006. Support vector machines with applications, Statistical Science 21(3): 322–336. https://doi.org/10.1214/088342306000000493
Montazeri-Gh, M.; Fotouhi, A. 2011. Traffic condition recognition using the k-means clustering method, Scientia Iranica: Transactions B: Mechanical Engineering 18(4): 930–937. https://doi.org/10.1016/j.scient.2011.07.004
Okutani, I.; Stephanedes, Y. J. 1984. Dynamic prediction of traffic volume through Kalman filtering theory, Transportation Research Part B: Methodological 18(1): 1–11. https://doi.org/10.1016/0191-2615(84)90002-X
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Srinivasan, D.; Chan, C. W.; Balaji, P. G. 2009. Computational intelligence-based congestion prediction for a dynamic urban street network, Neurocomputing 72(10–12): 2710–2716. https://doi.org/10.1016/j.neucom.2009.01.005
Tettamanti, T.; Varga, I. 2012. Development of road traffic control by using integrated VISSIM-MATLAB simulation environment, Periodica Polytechnica Civil Engineering 56(1): 43–49. https://doi.org/10.3311/pp.ci.2012-1.05
Van Grol, H. J. M.; Danech-Pajouh, M.; Manfredi, S.; Whittaker, J. 1999. DACCORD: on-line travel time prediction, in World Transport Research: Selected Proceedings of the 8th World Conference on Transport Research, 12–17 July 1998, Antwerp, Belgium, 455–467.
Van Lint, J. W. C. 2004. Reliable Travel Time Prediction for Freeways: Bridging Artificial Neural Networks and Traffic Flow Theory: PhD Thesis. Delft University of Technology, Netherlands. 325 p.
Viharos, Z. J.; Monostori, L.; Vincze, T. 2002. Training and application of artificial neural networks with incomplete data, Lecture Notes in Computer Science 2358: 649–659. https://doi.org/10.1007/3-540-48035-8_63
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Vlahogianni, E. I.; Karlaftis, M. G.; Golias, J. C. 2005. Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach, Transportation Research Part C: Emerging Technologies 13(3): 211–234. https://doi.org/10.1016/j.trc.2005.04.007
Werbos, P. J. 1974. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences: PhD Thesis. Harvard University, Cambridge, US. 454 p.
Wiedemann, R. 1974. Simulation des Straßenverkehrsflusses. Schriftenreihe des Instituts für Verkehrswesen der Universität Karlsruhe, Deutschland (in German).
Williams, B.; Durvasula, P.; Brown, D. 1998. Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models, Transportation Research Record: Journal of the Transportation Research Board 1644: 132–141. https://doi.org/10.3141/1644-14
Yu, R.; Wang, G.; Zheng, J.; Wang, H. 2013. Urban road traffic condition pattern recognition based on support vector machine, Journal of Transportation Systems Engineering and Information Technology 13(1): 130−136. https://doi.org/10.1016/S1570-6672(13)60097-5
Zefreh, M. M.; Török, Á. 2016. Improving traffic flow characteristics by suppressing shared taxis maneuvers, Periodica Polytechnica Transportation Engineering 44(2): 69–74. https://doi.org/10.3311/PPtr.8226
Zhu, J. Z.; Cao, J. X.; Zhu, Y. 2014. Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections, Transportation Research Part C: Emerging Technologies 47(2): 139–154. https://doi.org/10.1016/j.trc.2014.06.011
Ben-Akiva, M. E. 1998. DynaMIT: a simulation-based system for traffic prediction and guidance generation, in TRISTAN III: Triennal Symposium on Transportation Analysis, 17 June 1998, San Juan, Porto-Rico, 1–14.
Billings, D.; Yang, J.-S. 2006. Application of the ARIMA models to urban roadway travel time prediction – a case study, 2006 IEEE International Conference on Systems, Man and Cybernetics, 8–11 October 2006, Taipei, Taiwan, 2529–2534. https://doi.org/10.1109/ICSMC.2006.385244
Buzási, A.; Csete, M. 2015. Sustainability indicators in assessing urban transport systems, Periodica Polytechnica Transportation Engineering 43(3): 138–145. https://doi.org/10.3311/PPtr.7825
Byun, H.; Lee, S.-W. 2002. Applications of support vector machines for pattern recognition: a survey, Lecture Notes in Computer Science 2388: 213–236. https://doi.org/10.1007/3-540-45665-1_17
Chen, Y.; Yang, B.; Meng, Q. 2012. Small-time scale network traffic prediction based on flexible neural tree, Applied Soft Computing 12(1): 274–279. https://doi.org/10.1016/j.asoc.2011.08.045
Csikós, A.; Tettamanti, T.; Varga, I. 2015a. Macroscopic modeling and control of emission in urban road traffic networks, Transport 30(2): 152–161. https://doi.org/10.3846/16484142.2015.1046137
Csikós, A.; Viharos, Z. J.; Kis, B. K.; Tettamanti, T.; Varga, I. 2015b. Traffic speed prediction method for urban networks – an ANN approach, in 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 3–5 June 2015, Budapest, Hungary, 102–108. https://doi.org/10.1109/MTITS.2015.7223243
Devijver, P. A.; Kittler, J. 1982. Pattern Recognition: a Statistical Approach. 1st edition. Prentice Hall. 480 p.
Dimitriou, L.; Tsekeris, T.; Stathopoulos, A. 2008. Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow, Transportation Research Part C: Emerging Technologies 16(5): 554–573. https://doi.org/10.1016/j.trc.2007.11.003
Dougherty, M. S.; Cobbett, M. R. 1997. Short-term inter-urban traffic forecasts using neural networks, International Journal of Forecasting 13(1): 21–31. https://doi.org/10.1016/S0169-2070(96)00697-8
Fei, X.; Lu, C.-C.; Liu, K. 2011. A Bayesian dynamic linear model approach for real-time short-term freeway travel time prediction, Transportation Research Part C: Emerging Technologies 19(6): 1306–1318. https://doi.org/10.1016/j.trc.2010.10.005
Ficzere, P.; Ultmann, Z.; Török, Á. 2014. Time–space analysis of transport system using different mapping methods, Transport 29(3): 278–284. https://doi.org/10.3846/16484142.2014.916747
Fusco, G.; Colombaroni, C.; Comelli, L.; Isaenko, N. 2015. Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models, in 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 3–5 June 2015, Budapest, Hungary, 93–101. https://doi.org/10.1109/MTITS.2015.7223242
Gastaldi, M.; Gecchele, G.; Rossi, R. 2014. Estimation of annual average daily traffic from one-week traffic counts. a combined ANN-fuzzy approach, Transportation Research Part C: Emerging Technologies 47(1): 86–99. https://doi.org/10.1016/j.trc.2014.06.002
Guin, A. 2006. Travel time prediction using a seasonal autoregressive integrated moving average time series model, in 2006 IEEE Intelligent Transportation Systems Conference, 17–20 September 2016, Toronto, Canada, 493–498. https://doi.org/10.1109/ITSC.2006.1706789
Guo, J.; Huang, W.; Williams, B. M. 2014. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification, Transportation Research Part C: Emerging Technologies 43(1): 50–64. https://doi.org/10.1016/j.trc.2014.02.006
Kumar, K.; Parida, M.; Katiyar, V. K. 2013. Short term traffic flow prediction for a non urban highway using artificial neural network, Procedia – Social and Behavioral Sciences 104: 755–764. https://doi.org/10.1016/j.sbspro.2013.11.170
Kumar, K.; Parida, M.; Katiyar, V. K. 2015. Short term traffic flow prediction in heterogeneous condition using artificial neural network, Transport 30(4): 397–405. https://doi.org/10.3846/16484142.2013.818057
Li, Z.; Sun, D.; Jin, X.; Yu, D.; Zhang, Z. 2008. Pattern-based study on urban transportation system state classification and properties, Journal of Transportation Systems Engineering and Information Technology 8(5): 83–87. https://doi.org/10.1016/S1570-6672(08)60041-0
Lin, L.; Li, Y.; Sadek, A. 2013. A k nearest neighbor based local linear wavelet neural network model for on-line short-term traffic volume prediction, Procedia – Social and Behavioral Sciences 96: 2066–2077. https://doi.org/10.1016/j.sbspro.2013.08.233
Lin, S.; Xi, Y.; Yang, Y. 2008. Short-term traffic flow forecasting using macroscopic urban traffic network model, in 2008 11th International IEEE Conference on Intelligent Transportation Systems, 12–15 October 2008, Beijing, China, 134–138. https://doi.org/10.1109/ITSC.2008.4732567
Liu, H.; Van Lint, H. W. C.; Van Zuylen, H. J. 2006. Neural-network-based traffic flow model for urban arterial travel time prediction, in Transportation Research Board 86th Annual Meeting, 21–25 January 2007, Washington DC, US, 1–17.
Lo, S.-C. 2013. Expectation-maximization based algorithm for pattern recognition in traffic speed distribution, Mathematical and Computer Modelling 58(1–2): 449–456. https://doi.org/10.1016/j.mcm.2012.11.004
Lozano, A.; Manfredi, G.; Nieddu, L. 2009. An algorithm for the recognition of levels of congestion in road traffic problems, Mathematics and Computers in Simulation 79(6): 1926–1934. https://doi.org/10.1016/j.matcom.2007.06.008
McCulloch, W. S.; Pitts, W. 1943. A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics 5(4): 115–133. https://doi.org/10.1007/BF02478259
Moguerza, J. M.; Muñoz, A. 2006. Support vector machines with applications, Statistical Science 21(3): 322–336. https://doi.org/10.1214/088342306000000493
Montazeri-Gh, M.; Fotouhi, A. 2011. Traffic condition recognition using the k-means clustering method, Scientia Iranica: Transactions B: Mechanical Engineering 18(4): 930–937. https://doi.org/10.1016/j.scient.2011.07.004
Okutani, I.; Stephanedes, Y. J. 1984. Dynamic prediction of traffic volume through Kalman filtering theory, Transportation Research Part B: Methodological 18(1): 1–11. https://doi.org/10.1016/0191-2615(84)90002-X
Peng, H.; Long, F.; Ding, C. 2005. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8): 1226–1238. https://doi.org/10.1109/TPAMI.2005.159
Srinivasan, D.; Chan, C. W.; Balaji, P. G. 2009. Computational intelligence-based congestion prediction for a dynamic urban street network, Neurocomputing 72(10–12): 2710–2716. https://doi.org/10.1016/j.neucom.2009.01.005
Tettamanti, T.; Varga, I. 2012. Development of road traffic control by using integrated VISSIM-MATLAB simulation environment, Periodica Polytechnica Civil Engineering 56(1): 43–49. https://doi.org/10.3311/pp.ci.2012-1.05
Van Grol, H. J. M.; Danech-Pajouh, M.; Manfredi, S.; Whittaker, J. 1999. DACCORD: on-line travel time prediction, in World Transport Research: Selected Proceedings of the 8th World Conference on Transport Research, 12–17 July 1998, Antwerp, Belgium, 455–467.
Van Lint, J. W. C. 2004. Reliable Travel Time Prediction for Freeways: Bridging Artificial Neural Networks and Traffic Flow Theory: PhD Thesis. Delft University of Technology, Netherlands. 325 p.
Viharos, Z. J.; Monostori, L.; Vincze, T. 2002. Training and application of artificial neural networks with incomplete data, Lecture Notes in Computer Science 2358: 649–659. https://doi.org/10.1007/3-540-48035-8_63
Vlahogianni, E. I.; Karlaftis, M. G.; Golias, J. C. 2014. Shortterm traffic forecasting: where we are and where we’re going, Transportation Research Part C: Emerging Technologies 43(1): 3–19. https://doi.org/10.1016/j.trc.2014.01.005
Vlahogianni, E. I.; Karlaftis, M. G.; Golias, J. C. 2005. Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach, Transportation Research Part C: Emerging Technologies 13(3): 211–234. https://doi.org/10.1016/j.trc.2005.04.007
Werbos, P. J. 1974. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences: PhD Thesis. Harvard University, Cambridge, US. 454 p.
Wiedemann, R. 1974. Simulation des Straßenverkehrsflusses. Schriftenreihe des Instituts für Verkehrswesen der Universität Karlsruhe, Deutschland (in German).
Williams, B.; Durvasula, P.; Brown, D. 1998. Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models, Transportation Research Record: Journal of the Transportation Research Board 1644: 132–141. https://doi.org/10.3141/1644-14
Yu, R.; Wang, G.; Zheng, J.; Wang, H. 2013. Urban road traffic condition pattern recognition based on support vector machine, Journal of Transportation Systems Engineering and Information Technology 13(1): 130−136. https://doi.org/10.1016/S1570-6672(13)60097-5
Zefreh, M. M.; Török, Á. 2016. Improving traffic flow characteristics by suppressing shared taxis maneuvers, Periodica Polytechnica Transportation Engineering 44(2): 69–74. https://doi.org/10.3311/PPtr.8226
Zhu, J. Z.; Cao, J. X.; Zhu, Y. 2014. Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections, Transportation Research Part C: Emerging Technologies 47(2): 139–154. https://doi.org/10.1016/j.trc.2014.06.011