A New Approach to the Weights of Predict Traffic Flow Based on Internet of Things Technology

Jia Yang Li; Bei Lei Li; Zhi Hua Liu

doi:10.4028/www.scientific.net/AMR.1006-1007.521

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1006-1007A New Approach to the Weights of Predict Traffic...

A New Approach to the Weights of Predict Traffic Flow Based on Internet of Things Technology

Abstract:

Prediction of traffic flow is an important content of intelligent transportation system and the basic of traffic organization. Due to the poor accuracy of single predicted methods, this paper used Grey relational analysis to determine how important of each predicted methods in combination model. Using test methods to try to get the best weight set. By a case, the conclusion showed this weighted method was very effective.

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Periodical:

Advanced Materials Research (Volumes 1006-1007)

Pages:

521-524

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1006-1007.521

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Online since:

August 2014

Authors:

Jia Yang Li, Bei Lei Li, Zhi Hua Liu

Keywords:

Grey Relational Analysis, Internet of Things Technology, RBP Neural Networks, Traffic Flow

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References

[1] Jiayang Li. Grey Relational Analysis Method for Urban Logistics. Applied Mechanics and Materials Vols. 321-324 (2013) : 3065-3068.

DOI: 10.4028/www.scientific.net/amm.321-324.3065

Google Scholar

[2] Jiayang Li. Analysis Improtant Factors Influencing Driver Decision-Making Based on The Principal Component Method. IC3ME2014.

Google Scholar

[3] Dan Wang, Yuanwei Jing, Siying Zhang. Traffic dynamics based on a traffic awareness routing strategy on scale-free networks [J]. Physica A - Statistical Mechanics and Its Applications, Vol. 387, 2008(12): 3001-3007.

DOI: 10.1016/j.physa.2008.01.085

Google Scholar

[4] Dan Wang, Zhen Li. On traffic pricing model based on incentive stackelberg strategies for communication networks[J]. Communications in Computer and Information Science, Vol. 288( 2012): 498–505.

DOI: 10.1007/978-3-642-31965-5_58

Google Scholar