Abstract
Short-term traffic flow prediction is an important task for intelligent transportation systems. Conventional time series based approaches such as ARIMA can hardly reflect the inter-dependence of related roads. Other parametric or nonparametric methods do not take full advantage of the spatial temporal features. Moreover, some machine learning models are still not investigated in solving this problem. To fill this gap, in this paper we propose ExtTra: an extremely randomized trees based approach for short-term traffic flow prediction. To the best of our knowledge, our work is the first effort to apply the extremely randomized trees model on the traffic flow prediction problem. Moreover, our approach incorporates new spatial temporal features which were not considered in previous studies. Experimental results show that our approach significantly outperforms the baselines in prediction accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hamed, M.M., Al-Masaeid, H.R., Said, Z.M.B.: Short-term prediction of traffic volume in urban arterials. J. Transp. Eng. 121, 249–254 (1995)
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J. Transp. Eng. 129, 664–672 (2003)
Kumar, S.V., Vanajakshi, L.: Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur. Transp. Res. Rev. 7, 21 (2015)
Pascale, A., Nicoli, M.: Adaptive Bayesian network for traffic flow prediction. In: 2011 IEEE Statistical Signal Processing Workshop (SSP), pp. 177–180 (2011)
Wu, Y.-J., Chen, F., Lu, C.-T., Yang, S.: Urban traffic flow prediction using a spatio-temporal random effects model. J. Intell. Transp. Syst. 20, 282–293 (2016)
Rajabzadeh, Y., Rezaie, A.H., Amindavar, H.: Short-term traffic flow prediction using time-varying Vasicek model. Transp. Res. Part C Emerg. Technol. 74, 168–181 (2017)
Jeong, Y.S., Byon, Y.J., Castro-Neto, M.M., Easa, S.M.: Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 14, 1700–1707 (2013)
Dell’Acqua, P., Bellotti, F., Berta, R., Gloria, A.D.: Time-aware multivariate nearest neighbor regression methods for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 16, 3393–3402 (2015)
Xia, D., Wang, B., Li, H., Li, Y., Zhang, Z.: A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting. Neurocomputing 179, 246–263 (2016)
Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15, 2191–2201 (2014)
Yu, H., Wu, Z., Wang, S., Wang, Y., Ma, X.: Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks. Sensors 17, 1501 (2017)
Du, S., Li, T., Gong, X., Yang, Y., Horng, S.J.: Traffic flow forecasting based on hybrid deep learning framework. In: 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 1–6 (2017)
Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI, pp. 1655–1661 (2017)
Wang, D., Xiong, J., Xiao, Z., Li, X.: Short-term traffic flow prediction based on ensemble real-time sequential extreme learning machine under non-stationary condition. In: IEEE 83rd Vehicular Technology Conference (VTC Spring), pp. 1–5 (2016)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63, 3–42 (2006)
Yang, H.F., Dillon, T.S., Chen, Y.P.P.: Optimized structure of the traffic flow forecasting model with a deep learning approach. IEEE Trans. Neural Netw. Learn. Syst. 28, 2371–2381 (2017)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Acknowledgements
This work was supported in part by: National Natural Science Foundation of China (No. 61702059), Frontier and Application Foundation Research Program of Chongqing City (No. cstc2018jcyjAX0340), Chongqing Industrial Generic Technology Innovation Program (No. cstc2017zdcy-zdzxX0010), Guangxi Key Laboratory of Trusted Software (No. kx201702).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Shang, J., Yan, X., Feng, L., Dong, Z., Wang, H., Zhou, S. (2018). ExtTra: Short-Term Traffic Flow Prediction Based on Extremely Randomized Trees. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-04212-7_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04211-0
Online ISBN: 978-3-030-04212-7
eBook Packages: Computer ScienceComputer Science (R0)