Journal
MATHEMATICAL PROBLEMS IN ENGINEERING
Volume 2015, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2015/348036
Keywords
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Funding
- Twelfth Five-Year National Science & Technology Pillar Program [2014BAG01-B04]
- Beijing Science and Technology Plan [Z121100000312101]
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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.
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