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Lu, Hua-pu; Sun, Zhi-yuan; Qu, Wen-cong; Wang, Ling (2015)
Publisher: Hindawi Publishing Corporation
Journal: Mathematical Problems in Engineering
Languages: English
Types: Article
Subjects: TA1-2040, Mathematics, Engineering (General). Civil engineering (General), QA1-939, Article Subject

Classified by OpenAIRE into

arxiv: Computer Science::Networking and Internet Architecture
This paper focuses on the problems of short-term traffic flow forecasting. The main goal is to put forward traffic correlation model and real-time correction algorithm for traffic flow forecasting. Traffic correlation model is established based on the temporal-spatial-historical correlation characteristic of traffic big data. In order to simplify the traffic correlation model, this paper presents correction coefficients optimization algorithm. Considering multistate characteristic of traffic big data, a dynamic part is added to traffic correlation model. Real-time correction algorithm based on Fuzzy Neural Network is presented to overcome the nonlinear mapping problems. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling methods.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Peng, Y., Lei, M., Li, J. B., Peng, X. Y.. A novel hybridization of echo state networks and multiplicative seasonal ARIMA model for mobile communication traffic series forecasting. Neural Computing and Applications. 2014; 24 (3-4): 883-890
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    • Liu, X. W., Fang, X. M., Qin, Z. H., Ye, C., Xie, M.. A Short-term forecasting algorithm for network traffic based on chaos theory and SVM. Journal of Network and Systems Management. 2011; 19 (4): 427-447
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    • Zhang, Y., Liu, Y. C.. Analysis of peak and non-peak traffic forecasts using combined models. Journal of Advanced Transportation. 2011; 45 (1): 21-37
    • Lu, H.-P., Sun, Z.-Y., Qu, W.-C.. Big data-driven based real-time traffic flow state identification and prediction.
    • Dong, H. H., Sun, X. L., Jia, L. M., Qin, Y.. Multimode traffic volume prediction model. Journal of Jilin University. 2011; 41 (3): 645-649
    • Lu, H. P., Sui, Y. G., Guo, M., Li, R. M.. Urban Mixed Traffic Flow Analysis Model and Method. 2009
    • Sun, L. G., Li, R. M., Dong, S., Lu, H. P.. Study on short-term traffic flow combined forecast method. Journal Wuhan University of Technology (Transportation Science & Engineering). 2010; 34 (5): 874-881
    • Transportation Research Board, null. 2010 Highway Capacity Manual. 2010
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