Abstract
In this paper, a plane moving average algorithm is proposed for solving the urban road flow forecasting problem. This new approach assembles information from relevant traffic time series and has the following advantages: (1) it integrates both individual and similar flow patterns in making prediction, (2) the training data set does not need to be large, (3) it has more generalization capabilities in predicting unpredictable and much complex urban traffic flow than previously used methods. To assess the new model, we have performed extensive experiments on a real data set, and the results give evidence of its superiority over existing methods.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Abdulhai, B., Porwal, H., Recker, W.: Short-term traffic flow prediction using neuro-genetic algorithms. ITS Journal-Intelligent Transportation Systems Journal 7(1), 3–41 (2002)
Chan, K.Y., Dillon, T., Chang, E., Singh, J.: Prediction of short-term traffic variables using intelligent swarm-based neural networks. IEEE Transactions on Control Systems Technology, 21(1), 263–274 (2013)
Chan, K.Y., Dillon, T.S., Singh, J., Chang, E.: Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and levenberg-marquardt algorithm. IEEE Transactions on Intelligent Transportation Systems 13(2), 644–654 (2012)
Chan, K.Y., Dillon, T.S., Singh, J., Chang, E.: Traffic flow forecasting neural networks based on exponential smoothing method. In: 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 376–381. IEEE (2011)
Chan, K.Y., Yiu, C.K.: Development of neural network based traffic flow predictors using pre-processed data. In: Optimization and Control Methods in Industrial Engineering and Construction, pp. 125–138. Springer (2014)
Chen, M., Liu, Y., Yu, X.: NLPMM: A Next Location Predictor with Markov Modeling. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part II. LNCS, vol. 8444, pp. 186–197. Springer, Heidelberg (2014)
Davarynejad, M., Wang, Y., Vrancken, J., van den Berg, J.: Multi-phase time series models for motorway flow forecasting. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 2033–2038. IEEE (2011)
Davis, G.A., Nihan, N.L.: Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering 117(2), 178–188 (1991)
Dia, H.: An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research 131(2), 253–261 (2001)
Gao, Q., Li, G .: A traffic prediction method based on ann and adaptive template matching (2011)
Guo, H., Xiao, X., Tang, Y.: Short-Term Traffic Flow Forecasting Based on Grey Delay Model. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds.) AICI 2012. LNCS, vol. 7530, pp. 357–364. Springer, Heidelberg (2012)
Hanke, J.E., Reitsch, A.G., Wichern, D.W.: Business forecasting. Prentice Hall Upper Saddle River, NJ (2001)
Papagiannaki, K., Taft, N., Zhang, Z.-L., Diot, C.: Long-term forecasting of internet backbone traffic: Observations and initial models. In: INFOCOM 2003, Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, vol. 2, pp. 1178–1188. IEEE (2003)
Papagiannaki, K., Taft, N., Zhang, Z.-L., Diot, C.: Long-term forecasting of internet backbone traffic. IEEE Transactions on Neural Networks 16(5), 1110–1124 (2005)
Shang, J., Zheng, Y., Tong, W., Chang, E., Yu, Y.: Inferring gas consumption and pollution emission of vehicles throughout a city. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1027–1036. ACM (2014)
Smith, B.L., Demetsky, M.J.: Traffic flow forecasting: comparison of modeling approaches. Journal of Transportation Engineering 123(4), 261–266 (1997)
Smith, B.L., Williams, B.M., Oswald, R.K.: Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies 10(4), 303–321 (2002)
Tan, M.-C., Wong, S.C., Xu, J.-M., Guan, Z.-R., Zhang, P.: An aggregation approach to short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems 10(1), 60–69 (2009)
Williams, B.M.: Multivariate vehicular traffic flow prediction: Evaluation of arimax modeling. Transportation Research Record: Journal of the Transportation Research Board 1776(1), 194–200 (2001)
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results. Journal of Transportation Engineering 129(6), 664–672 (2003)
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
Lv, L., Chen, M., Liu, Y., Yu, X. (2015). A Plane Moving Average Algorithm for Short-Term Traffic Flow Prediction. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-18032-8_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18031-1
Online ISBN: 978-3-319-18032-8
eBook Packages: Computer ScienceComputer Science (R0)