Abstract
Traffic flow forecasting is an important aspect of the ITS as accurate traffic predication can alleviate congestion, save traveling time and reduce economical loses. The forecasting process may rely on historical data, current data, or both, to forecast the traffic volume in the future. In this paper, we compare three different approaches in traffic forecasting, study the input data and output data for these approaches, as well as some general insights, and also propose BP neural network to estimate accurate traffic flow for a roadway section. By means of three layers-BP neutral network model, in which mechanism algorithm are used to preprocess the multi-source data, error data is eliminated, multi-source data fusion is realized and accurate traffic forecasting is achieved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Weng, X.X., Du, G.I.: Hybrid Elman Neural Network Model for Short-Term Traffic Prediction, pp. 235–280. IASTED International Conference Press, Anaheim (2006)
Research Institute of Highway Ministry of Transport. Research Report of China Intelligent Transportation System Framework. Research Institute of Highway Ministry of Transport Press, Beijing (2010)
Yua, R., Laoa, Y., Maa, X., Wanga, Y.: Short-term traffic flow forecasting for freeway incident-induced delay estimation. J. Intell. Transp. Syst. 18, 254–263 (2015). USA
Ke L., Jianjun,,T. Yingyuan, L: A short-term traffic flow forecasting method based on MapReduce, pp. 174–179. China (2015)
Cong, W.: An Internet Traffic Forecasting Model Adopting Radical Based on Function Neural Network Optimized by Genetic Algorithm, pp. 385–388. IEEE Press, Australia (2008)
Liang, Z.: Short-Term Traffic Flow Prediction Based on Interval Type-2 Fuzzy Neural Networks. IEEE Press, China (2010)
Chrobok, R., Kaumann, O., Wahle, J.: Different methods of traffic forecast based on real data. J. Eur. J. Oper. Res. 155, 558–568 (2004)
Cetin, M., Comert, G.: Short-term traffic flow prediction with regime-switching models. J. Transp. Res. Rec. 1965, 23–31 (2006)
Eleni, I.V., Matthew, G.K., John, C.G.: Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. J. Transp. Res. Part C Emerg. Technol. 13, 211–234 (2005)
Chang, G., Tongming, G.: Comparison of Missing Data Imputation Methods for Traffic Flow, pp. 639–642. IEEE Press, USA (2011)
An, Y., Song, Q.: Short-term traffic flow forecasting via echo state neural networks, pp. 844–847. In: Ding, Y., Wang, H., Xiong, N., Hao, K., Wang, L. (Eds.) Proceedings of 7th ICNC, Press, China (2011)
Tao, Z., Lifang, H., Zhixin, L., Yuejie, Z.: Nonparametric Regression for the Short-Term Traffic Flow Forecasting. MACE Press, USA (2010)
Kamga, C.N., Mouskos, K.C., Paaswell, R.E.: A methodology to estimate travel time using dynamic traffic assignment (DTA) under incident conditions. J. Transp. Res. Part C Emer. Technol. 19, 1215–1224 (2011)
Cheevarunothai, P., Zhang, G., Wang, Y.: Using precise time offset to improve freeway vehicle delay estimates. J. Intell. Transp. Syst. Technol. Plan. Oper. 16, 82–93 (2012)
Chung, Y.: Quantification of non-recurrent congestion delay caused by freeway accidents and analysis of causal factors. Transp. Res. Rec. J. Transp. Res. Board 2229, 8–18 (2011)
Horvitz, E., Apacible, J., Sarin, R., Liao, L..: Prediction, expectation, and surprise: methods, designs, and study of a deployed traffic forecasting service. CoRR, vol. abs/1207.1352, USA (2012)
Lippi, M., Bertini, M., Frasconi, P.: Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning. IEEE Press, New York (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, W., Xiao, R., Deng, J. (2015). Research of Traffic Flow Forecasting Based on the Information Fusion of BP Network Sequence. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-23862-3_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23861-6
Online ISBN: 978-3-319-23862-3
eBook Packages: Computer ScienceComputer Science (R0)