Abstract
Daily Electronic Toll Collection (ETC) traffic flow prediction is one of the fundamental processes in ETC management. The precise prediction of traffic flow provides instructions for transportation hub management solution planning and ETC lane construction. At present, some of studies are proposed in forecasting traffic flow. However, most studies of model presentation are in the form of mathematical expressions, and it is difficult to describe the trend accurately. Therefore, an ETC traffic flow prediction model based on k nearest neighbor searching (k-NN) and Back Propagation (BP) neural network is proposed, which takes the effect of external factors like holiday, the free of highway and weather etc. into consideration. The traffic flow data of highway ETC lane somewhere is used for prediction. The prediction results indicate that the total average absolute relative error is 5.01 %. The accuracy suggests its advantage in traffic flow prediction and on site application.
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
Han, C., Song, S., Wang, C.H.: A real-time short-term traffic flow adaptive forecasting method based on ARIMA model. J. Syst. Simul. 16(7), 1530–1532 (2004)
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(6), 664–672 (2003)
Ni, L., Chen, X., Huang, Q.: ARIMA model for traffic flow prediction based on wavelet analysis. In: 2010 2nd International Conference on Information Science and Engineering (ICISE), pp. 1028–1031. IEEE (2010)
Qiu, D.G., Yang, H.Y.: A short-term traffic flow forecast algorithm based on double seasonal time series. J SiChuan Univ. Eng. Sci. Ed. 45(05) (2013)
Zhang, T., Chen, X., Xie, M.P., et al.: K-NN based nonparametric regression method for short-term traffic flow forecasting. Syst. Eng. Theory Pract. 30(2), 376–384 (2010)
Huang, H.Q., Tang, T.H.: Short-term traffic flow forecasting based on ARIMA-ANN. In: 2007 IEEE International Conference on Control and Automation, ICCA 2007, pp. 2370–2373. IEEE (2007)
Zhang, C.B., Wan, P., Mei, C.H., et al.: Traffic flow characteristics and models of freeway under rain weather. J. Wuhan Univ. Technol. 35(3), 63–67 (2013)
Cools, M., Moons, E., Wets, G.: Investigating effect of holidays on daily traffic counts: time series approach. Transp. Res. Rec. J. Transp. Res. Board 2007(2019), 22–31 (2019)
Cao, X.L., Wang, S.P.: Short time wind power prediction based on multidimensional time-series and BP neural networks. Shanxi Electr. Power 42(4), 19–23 (2014)
Kadiyala, A., Kumar, A.: Multivariate time series models for prediction of air quality inside a public transportation bus using available software. Environ. Prog. Sustain. Energy 33(2), 337–341 (2014)
Bueno, L., Costa, P., Mendes, I., et al.: Evolving ensemble of fuzzy models for multivariate time series prediction. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015) (2015)
Han, J.W.: Data Mining Concept and Techniques. China Machine Press, Beijing (2006)
Ferhatosmanoglu, H., Tuncel, E., Agrawal, D., et al.: Approximate nearest neighbor searching in multimedia databases. In: International Conference on Data Engineering, pp. 503–511. IEEE Computer Society (2001)
Acknowledgments
The author would like to thank the members of the team for providing the helpful discussions and ideas. In addition, we would like to thank every teacher that provides instruction.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Chen, Y., Zhao, Y., Yan, P. (2016). Daily ETC Traffic Flow Time Series Prediction Based on k-NN and BP Neural Network. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 624. Springer, Singapore. https://doi.org/10.1007/978-981-10-2098-8_17
Download citation
DOI: https://doi.org/10.1007/978-981-10-2098-8_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2097-1
Online ISBN: 978-981-10-2098-8
eBook Packages: Computer ScienceComputer Science (R0)