-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition
Publication: Journal of Transportation Engineering
Volume 142, Issue 6
Abstract
One of the most critical functions of an intelligent transportation system (ITS) is to provide accurate and real-time prediction of traffic condition. This paper develops a short-term traffic condition prediction model based on the -nearest neighbor algorithm. In the prediction model, the time-varying and continuous characteristic of traffic flow is considered, and the multi-time-step prediction model is proposed based on the single-time-step model. To test the accuracy of the proposed multi-time-step prediction model, GPS data of taxis in Foshan city, China, are used. The results show that the multi-time-step prediction model with spatial-temporal parameters provides a good performance compared with the support vector machine (SVM) model, artificial neural network (ANN) model, real-time-data model, and history-data model. The results also appear to indicate that the proposed -nearest neighbor model is an effective approach in predicting the short-term traffic condition.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (71571026, 51578112, and 51208079), the Trans-Century Training Program Foundation for Talents from the Ministry of Education of China (NCET-12-0752), Liaoning Excellent Talents in University (LJQ2012045), and the Fundamental Research Funds for the Central Universities (3013-852019 and 3132015062).
References
Akbari, M., van Overloop, P. J., and Afshar, A. (2011). “Clustered k nearest neighbor algorithm for daily inflow forecasting.” Water Resour. Manage., 25(5), 1341–1357.
Boto-Giralda, D., Diaz-Pernas, F. J., Gonzalez-Ortega, D., Diez-Higuera, J. F., Anton-Rodriguez, M., and Martinez-Zarzuela, M. (2010). “Wavelet-based denoising for traffic volume time series forecasting with self-organizing neural networks.” Comput.-Aided Civ. Infrastruct. Eng., 25(7), 530–545.
Chang, H., Lee, Y., Yoon, B., and Baek, S. (2012). “Dynamic near-term traffic flow prediction: System-oriented approach based on past experiences.” IET Intell. Transp. Syst., 6(3), 292–305.
Coric, V., Djuric, N., and Vucetic, S. (2012). “Traffic state estimation from aggregated measurements with signal reconstruction techniques.” Transp. Res. Rec., 2315, 121–130.
Jiang, X., Adeli, H. (2005). “Dynamic wavelet neural network model for traffic flow forecasting.” J. Transp. Eng., 771–779.
Kamarianakis, Y., and Prastacos, P. (2003). “Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches.” Transp. Res. Rec., 1857, 74–84.
Kirby, H. R., Watson, S. M., and Dougherty, M. S. (1997). “Should we use neural networks or statistical models for short-term motorway traffic forecasting?” Int. J. Forecasting, 13(1), 43–50.
Lee, K. L., and Billings, S. A. (2003). “A new direct approach of computing multi-step ahead predictions for non-linear models.” J. Control, 76(8), 810–822.
Li, R., and Rose, G. (2011). “Incorporating uncertainty into short-term travel time predictions.” Transp. Res. Part C, 19(6), 1006–1018.
Lin, L., Wang, Q., and Sadek, A. K. (2013). “Short-term forecasting of traffic volume evaluating models based on multiple data sets and data diagnosis measures.” Transp. Res. Rec., 2392, 40–47.
Meng, M., Shao, C. F., and Wong, Y. D. (2015). “A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques.” J. Central South Univ., 22(2), 779–786.
Min, W. L., and Wynter, L. (2011). “Real-time road traffic prediction with spatio-temporal correlations.” Transp. Res. Part C, 19(4), 606–616.
Okutani, I., and Stephanedes, Y. J. (1984). “Dynamic prediction of traffic volume through Kalman filtering theory.” Transp. Res. Part B, 18(1), 1–11.
Parlos, A. G., Rais, O. T., and Atiya, A. F. (2000). “Multi-step-ahead prediction using dynamic recurrent neural networks.” Neural Networks, 13(7), 765–786.
Smith, B. L., and Demetsky, M. J. (1996). “Multiple-interval freeway traffic flow forecasting.” Transp. Res. Rec., 1554, 136–141.
Smith, B. L., and Demetsky, M. J. (1997). “Traffic flow forecasting: Comparison of modeling approaches.” J. Transp. Eng., 261–266.
Smith, B. L., Williams, B. M., and Oswald, R. K. (2002). “Comparison of parametric and nonparametric models for traffic flow forecasting.” Transp. Res. Part C, 10(4), 303–321.
Tan, M. C., Wong, S. C., Xu, J. M., Guan, Z. R., and Zhang, P. (2009). “An aggregation approach to short-term traffic flow prediction.” Intell. Transp. Syst., 10(1), 60–69.
Turochy, R. E. (2006). “Enhancing short-term traffic forecasting with traffic condition information.” J. Transp. Eng., 469–474.
Vlahogianni, E. I., Karlaftis, M. G., and Golias, J. C. (2007). “Spatio-temporal short-term urban traffic volume forecasting using genetically optimized modular networks.” Comput.-Aided Civ. Infrastruct. Eng., 22(5), 317–325.
Wei, Y., and Chen, M. C. (2012). “Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks.” Transp. Res. Part C, 21(1), 148–162.
William, H. K., Tang, Y. F., and Tam, M. L. (2006). “Comparison of two non-parametric models for daily traffic forecasting in Hong Kong.” J. Forecasting, 25(3), 173–192.
Xu, D. W., Dong, H. H., and Li, H. J. (2015). “The estimation of road traffic based on compressive sensing.” Transp. B-Transp. Dyn., 3(2), 131–152.
Yao, B. Z., Hu, P., Zhang, M. H., and Jin, M. Q. (2014a). “A support vector machine with the Tabu search algorithm for freeway incident detection.” Int. J. Appl. Math. Comput. Sci., 24(2), 397–404.
Yao, B. Z., Wang, Z., Zhang, M. H., Hu, P., and Yan, X. X. (2015). “Hybrid model for prediction of real-time traffic flow.” Proc. Inst. Civ. Eng. Transp., in press.
Yao, B. Z., Yao, J. B., Zhang, M. H., and Yu, L. (2014b). “Improved support vector machine regression in multi-step-ahead prediction for rock displacement surrounding a tunnel.” Scientia Iranica, 21(4), 1309–1316.
Yoon, B., and Chang, H. (2014). “Potentialities of data-driven nonparametric regression in urban signalized traffic flow forecasting.” J. Transp. Eng., 04014027.
Yu, B., Yang, Z. Z., Chen, K., and Yu, B. (2010). “Hybrid model for prediction of bus arrival times at next station.” J. Adv. Transp., 44(3), 193–204.
Zheng, W. Z., Lee, D. H., and Shi, Q. X. (2006). “Short-term freeway traffic flow prediction: Bayesian combined neural network approach.” J. Transp. Eng., 114–121.
Zhong, H. L., Kuang, C. J., and Huang, X. Y. (2012). “Traffic flow prediction algorithm based on historical frequent pattern.” Comput. Eng. Des., 33(4), 1547–1552.
Zuo, W., Zhang, D., and Wang, K. (2008). “On kernel difference-weighted -nearest neighbor classification.” Pattern Anal. Appl., 11(3–4), 247–257.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 142 • Issue 6 • June 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Jul 25, 2014
Accepted: Sep 22, 2015
Published online: Feb 16, 2016
Published in print: Jun 1, 2016
Discussion open until: Jul 16, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.