Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set

  • Erdem Doğan Kırıkkale University, Engineering Faculty
Keywords: traffic prediction, training set, short-term prediction, k-means, artificial neural networks


Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still working on novel approaches. This study aims to develop an algorithm that dynamically updates the training set of models in order to make more accurate predictions. For this purpose, an algorithm called Periodic Clustering and Prediction (PCP) has been developed for use in short-term traffic forecasting. In this study, PCP was used to improve Artificial Neural Networks (ANN) predictive performance by improving the training set of ANN to predict short-term traffic flow using selected clusters. A large amount of traffic data collected from the US and UK motorways was used to determine the PCP ability to increase the ANN performance. The robustness of the proposed approach was determined by the performance measures used in the literature and the mean prediction errors of PCP were significantly below other approaches. In addition, the studies showed that the percentage errors of PCP predictions decreased in response to increasing traffic flow values. Considering the obtained positive results, this method can be used in real-time traffic control systems and in different areas needed.

Author Biography

Erdem Doğan, Kırıkkale University, Engineering Faculty

Department of Civil Engineering


Vlahogianni EI, Karlaftis MG, Golias JC. Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C: Emerging Technologies. 2014;43: 3-19. Available from: doi:10.1016/j.trc.2014.01.005

Guo J, Huang W, Williams BM. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C: Emerging Technologies. 2014;43: 50-64. Available from: doi:10.1016/j.trc.2014.02.006

Habtemichael FG, Cetin M. Short-term traffic flow rate forecasting based on identifying similar traffic patterns. Transportation Research Part C: Emerging Technologies. 2016;66: 61-78. Available from: doi:10.1016/j.trc.2015.08.017

Ahmed MS, Cook AR. Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques. Transportation Research Record. 1979. Available from: doi:10.3141/2024-03

Vlahogianni EI, Golias JC, Karlaftis MG. Short-term traffic forecasting: Overview of objectives and methods. Transport Reviews. 2004;24(5): 533-557. Available from: doi:10.1080/0144164042000195072

Van Lint H, van Hinsbergen C. Short-term traffic and travel time prediction models. Artificial Intelligence Applications to Critical Transportation Issues; 2012. p. 22-41.

Nikovski D, Nishiuma N, Goto Y, Kumazawa H. Univariate short-term prediction of road travel times. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; 2005. p. 1074-1079. Available from: doi:10.1109/ITSC.2005.1520200

Huisken G, van Berkum EC. A Comparative Analysis of Short-Range Travel Time Prediction Methods. TRB 2003 Annual Meeting CD-ROM. 2003; 21 p.

Wu CH, Ho JM, Lee DT. Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems. 2004. p. 276-281. Available from: doi:10.1109/TITS.2004.837813

Park D, Rilett LR. Forecasting multiple-period freeway link travel times using modular neural networks. Transportation Research Record: Journal of the Transportation Research Board. 1998;1617: 163-170.

Kamarianakis Y, Prastacos P. Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches. Transportation Research Record: Journal of the Transportation Research Board. 2003;1857(1): 74-84. Available from: doi:10.3141/1857-09

Eglese R, Maden W, Slater A. A Road TimetableTM to aid vehicle routing and scheduling. Computers and Operations Research. 2006;33(12): 3508-3519. Available from: doi:10.1016/j.cor.2005.03.029

Hobeika AG, Kim C. Traffic-Flow-Prediction Systems Based on Upstream Traffic a.G. Vehicle Navigation and Information Systems Conference, 1994. Proceedings. IEEE; 1990. p. 345-350.

Smith BL, Williams BM, Keith Oswald R. Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies. 2002;10(4): 303-321. Available from: doi:10.1016/S0968-090X(02)00009-8

Chung E. Classification of traffic pattern. Proc. of the 11th World Congress on ITS; 2003. p. 4-6.

Wild D. Short-term forecasting based on a transformation and classification of traffic volume time series. International Journal of Forecasting. 1997;13(1): 63-72. Available from: doi:10.1016/S0169-2070(96)00701-7

Williams BM, Hoel LA. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering. 2003;129(6): 664-672.

Guo J, Williams BM, Smith BL. Data Collection Time Intervals for Stochastic Short-Term Traffic Flow Forecasting. Transportation Research Record: Journal of the Transportation Research Board. 2008;2024(1): 18-26. Available from: doi:10.3141/2024-03

Shekhar S, Williams B. Adaptive seasonal time series models for forecasting short-term traffic flow. Transportation Research Record: Journal of the Transportation Research Board. 2008;(2024): 116-125.

Zeng D, Xu J, Gu J, Liu L, Xu G. Short term traffic flow prediction using hybrid ARIMA and ANN models. Proceedings - 2008 Workshop on Power Electronics and Intelligent Transportation System, PEITS 2008; 2008. Available from: doi:10.1109/PEITS.2008.135

Lin SL, Huang HQ, Zhu DQ, Wang TZ. The application of space-time arima model on traffic flow forecasting. Proceedings of the 2009 International Conference on Machine Learning and Cybernetics. IEEE; 2009. p. 3408-3412. Available from: doi:10.1109/ICMLC.2009.5212785

Chen C, Hu J, Meng Q, Zhang Y. Short-time traffic flow prediction with ARIMA-GARCH model. Intelligent Vehicles Symposium (IV), 2011 IEEE. IEEE; 2011. p. 607-612.

Zhou B, He D, Sun Z. Traffic predictability based on ARIMA/GARCH model. Next Generation Internet Design and Engineering, 2006. NGI’06. 2006 2nd Conference on. IEEE; 2006. 8 p.

Guo J, Huang W, Williams BM. Integrated Heteroscedasticity Test for Vehicular Traffic Condition Series. Journal of Transportation Engineering. 2012;138(9): 1161-1170. Available from: doi:10.1061/(ASCE)TE.1943-5436.0000420

Yang J-S. Travel time prediction using the GPS test vehicle and Kalman filtering techniques. Proceedings of the 2005, American Control Conference, 2005; 2005. p. 2128-2133. Available from: doi:10.1109/ACC.2005.1470285

Lin W-H. A Gaussian maximum likelihood formulation for short-term forecasting of traffic flow. ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585). 2001; p. 150-155. Available from: doi:10.1109/ITSC.2001.948646

Stathopoulos A, Karlaftis MG. A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C: Emerging Technologies. 2003;11(2): 121-135. Available from: doi:10.1016/S0968-090X(03)00004-4

Dunne S, Ghosh B. Regime-Based Short-Term Multivariate Traffic Condition Forecasting Algorithm. Journal of Transportation Engineering. 2012;138(4): 455-466. Available from: doi:10.1061/(ASCE)TE.1943-5436.0000337

Zargari SA, Siabil SZ, Alavi AH, Gandomi AH. A computational intelligence-based approach for short-term traffic flow prediction. Expert Systems. 2012;29(2): 124-142. Available from: doi:10.1111/j.1468-0394.2010.00567.x

Kumar K, Parida M, Katiyar VK. Short term traffic flow prediction in heterogeneous condition using artificial neural network. Transport. 2015;30(4): 397-405. Available from: doi:10.3846/16484142.2013.818057

Clark S. Traffic Prediction Using Multivariate Nonparametric Regression. Journal of Transportation Engineering. 2003;129(2): 161-168. Available from: doi:10.1061/(ASCE)0733-947X(2003)129:2(161)

Polson NG, Sokolov VO. Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies. 2017;79: 1-17. Available from: doi:

Ermagun A, Levinson D. Spatiotemporal short-term traffic forecasting using the network weight matrix and Emerging Technologies. 2019;104: 38-52.

Chen Y, Yang B, Meng Q, Zhao Y, Abraham A. Time-series forecasting using a system of ordinary differential equations. Information Sciences. 2011;181(1): 106-114. Available from: doi:10.1016/j.ins.2010.09.006

Hong WC. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing. 2011;74(12-13): 2096-2107. Available from: doi:10.1016/j.neucom.2010.12.032

Hong WC, Dong Y, Zheng F, Lai CY. Forecasting urban traffic flow by SVR with continuous ACO. Applied Mathematical Modelling. 2011;35(3): 1282-1291. Available from: doi:10.1016/j.apm.2010.09.005

Hong WC, Dong Y, Zheng F, Wei SY. Hybrid evolutionary algorithms in a SVR traffic flow forecasting

model. Applied Mathematics and Computation. 2011;217(15): 6733-6747. Available from: doi:10.1016/j.amc.2011.01.073

Abdi J, Moshiri B, Abdulhai B, Sedigh AK. Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm. Engineering Applications of Artificial Intelligence. 2012;25(5): 1022-1042. Available from: doi:10.1016/j.engappai.2011.09.011

Zhang Y, Zhang Y, Haghani A. A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model. Transportation Research Part C: Emerging Technologies. 2014;43: 65-78. Available from: doi:10.1016/j.trc.2013.11.011

Feng X, Ling X, Zheng H, Chen Z, Xu Y. Adaptive Multi-Kernel SVM With Spatial-Temporal Correlation for Short-Term Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems. 2018; p. 1-13. Available from: doi:10.1109/TITS.2018.2854913

Vlahogianni EI, Karlaftis MG, Golias JC, Kourbelis ND. Pattern-Based Short-Term Urban Traffic Predictor. 2006 IEEE Intelligent Transportation Systems Conference. 2006; p. 389-393. Available from: doi:10.1109/ITSC.2006.1706772

Yuan Z, Zhang W, Yang M. A Short-term Traffic Flow Prediction Approach of Neural Network Based on Cluster Analysis. DEStech Transactions on Engineering and Technology Research. 2016;(iceta).

Lin F, Xu Y, Yang Y, Ma H. A Spatial-Temporal Hybrid Model for Short-Term Traffic Prediction. Mathematical Problems in Engineering. Hindawi; 2019; Article ID 4858546. 12 p.

Song Z, Guo Y, Wu Y, Ma J. Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model. PloS one. 2019;14(6): e0218626.

Hou Q, Leng J, Ma G, Liu W, Cheng Y. An adaptive hybrid model for short-term urban traffic flow prediction. Physica A: Statistical Mechanics and its Applications. 2019;527: 121065.

Desch CH. Conservation of natural resources. Nature. 1941;148(3758): 547-549. Available from: doi:10.1038/148547a0

Hampel FR. A General Qualitative Definition of Robustness. The Annals of Mathematical Statistics. 1971;42(6): 1887-1896. Available from: doi:10.1214/aoms/1177693054

Hampel FR. The influence curve and its role in robust estimation. Journal of the American Statistical Association. 1974;69(346): 383-393. Available from: doi:10.1080/01621459.1974.10482962

Davies L, Gather U. The identification of multiple outliers. Journal of the American Statistical Association. 1993;88(423): 782-792. Available from: doi:10.1080/01621459.1993.10476339

Pearson RK. Outliers in process modeling and identification. IEEE Transactions on Control Systems Technology. 2002;10(1): 55-63. Available from: doi:10.1109/87.974338

Cleveland WS. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association. 1979;74(368): 829-836. Available from: doi:10.1080/01621459.1979.10481038

Cleveland WS. Lowess: A program for smoothing scatterplots by robust locally weighted regression. American Statistician. 1981;35(1): 54-55. Available from: doi:10.1080/00031305.1981.10479306_3

Cleveland WS, Devlin SJ. Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association. 1988;83(403): 596-610. Available from: doi:10.1080/01621459.1988.10478639

MacQueen J. Some Methods for classification and Analysis of Multivariate Observations. 5th Berkeley Symposium on Mathematical Statistics and Probability 1967; 1967. p. 281-297. Available from: doi:citeulike-article-id:6083430

Forgy EW. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics. 1965;21(3): 768-769. Available from: doi:10.1007/s00442-008-1028-8

Yin X, Zhang J, Wang X. Sequential injection analysis system for the determination of arsenic by hydride generation atomic absorption spectrometry. Fenxi Huaxue. 2004;32(10): 1365-1367. Available from: doi:10.1017/CBO9781107415324.004

Marquardt DW. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of the Society for Industrial and Applied Mathematics. 1963;11(2): 431-441. Available from: doi:10.1137/0111030

Willmott CJ, Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research. 2005;30(1): 79-82.

Willmott CJ. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society. 1982;63(11): 1309-1313.

Hyndman RJ. Another look at measures of forecast accuracy for intermittent demand. Foresight: the International Journal of Applied Forecasting. 2006;4(4): 43-46.

How to Cite
Doğan E. Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set. Promet [Internet]. 2020Feb.6 [cited 2024Jun.21];32(1):65-8. Available from: