Abstract
Under the real circumstances of traffic flow’s grey features and traffic system’s delay effect, this paper construct a grey delay model (GM (1,1,τ) model) and investigate relevant properties, Finally, we complete a traffic experiment on the section of Youyi Avenue, estimate the delay time of the system and establish the delay model. The comparison between the delay model with the GM(1,1) model without considering practical conditions of the traffic system shows that incorporating existing delay effect of the traffic system into the model can reasonably reflect the characteristics of the system and provide satisfactory predictions.
This work was supported by the National Natural Science Foundation of China under Grant 70971103 and the General Education Program (GEP) Requirements in the Humanities of Social Sciences under Grant 11YJC630155.
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References
Queen, C.M., Albers, C.J.: Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network. Journal of the American Statistical Association 104, 669–681 (2009)
Tan, M.C., Wong, S.C.: An Aggregation Approach to Short-term Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems, 66–69 (2009)
Xie, Y.C., Zhao, K.G.: Gaussian Processes for Short-term Traffic Volume Forecasting. Transportation Research Record, 69–78 (2010)
Castro-Neto, M., Jeong, Y.S.: Online-SVR for Short-term Traffic Flow Prediction Under Typical and a Typical Traffic Conditions. Expert System with Applications 36(3), 6164–6173 (2009)
Reuter, U., Moller, B.: Artificial Neural Networks for Forecasting of Fuzzy Time Series. Computer-Aided Civil and Infrastructure Engineering 25(5), 363–374 (2010)
Huang, S., Sadek, A.W.: A Novel Forecasting Approach Inspired by Human Memory: The Example of Short-term Traffic Volume Forecasting. Transportation Research Part C-merging Technologies 17(5), 510–525 (2009)
Fang, Z.G., Liu, S.F.: The Military Traffic Flows Distribute Model Research of Maximum Gray Information Entropy Based on Maximum Concealment. Chinese Journal of Management Science 11(3), 56–61 (2003)
Sun, Y., Chen, S.F., Zhou, Z.Q.: Application of Grey Models to Traffic Flow Prediction at Non-detector Intersections. Journal of Southeast University (Natural Science Edition) 32(2), 256–258 (2002)
Deng, Z.L., Li, Q., Chen, Q.: Research on Short Time Traffic Flow Prediction Based on Gray System Theory. Technology of Highway and Transport 2(1), 117–119 (2006)
Dai, L.J., Huang, H.O.: Traffic Flow Forecast Model Based on Residual Correction of Grey System Theory. China Water Transport 8(7), 127–128 (2007)
Deng, J.L.: Grey Theory Foundation. Huazhong University of Technology Press, Wuhan (2002)
Dai, J.F., Ma, J.X.: Traffic Engineering Conspectus. China Communications Press, Beijing (2006)
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Guo, H., Xiao, X., Tang, Y. (2012). Short-Term Traffic Flow Forecasting Based on Grey Delay Model. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_45
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DOI: https://doi.org/10.1007/978-3-642-33478-8_45
Publisher Name: Springer, Berlin, Heidelberg
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