Computational Intelligence and Neuroscience
Volume 2016, Article ID 1874945, 16 pages
http://dx.doi.org/10.1155/2016/1874945
Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research
School of Software, Beijing Institute of Technology, Beijing 100081, China
Received 3 February 2016; Revised 14 July 2016; Accepted 8 September 2016
Academic Editor: Adel Alimi
Copyright © 2016 Haijing Tang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction.