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Real-Time Corrected Traffic Correlation Model for Traffic Flow Forecasting

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  • Hua-pu Lu
  • Zhi-yuan Sun
  • Wen-cong Qu
  • Ling Wang

Abstract

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.

Suggested Citation

  • Hua-pu Lu & Zhi-yuan Sun & Wen-cong Qu & Ling Wang, 2015. "Real-Time Corrected Traffic Correlation Model for Traffic Flow Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-7, September.
    • Handle: RePEc:hin:jnlmpe:348036
    DOI: 10.1155/2015/348036
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