Predicting Traffic Congestion Time Based on Kalman Filter Algorithm


Predicting Traffic Congestion Time Based on Kalman Filter Algorithm


Tianlong Wang1*, Xiaorui Tao2, Jiamei Zhang3, Yulei Li4

1College of Civil Engineering & Architecture, China Three Gorges University. 2College of Economics & Management, China Three Gorges University. 3College of Computer and Information Technology, China Three Gorges University. 4College of Electrical Engineering & New Energy, China Three Gorges University, Yichang, 443002, China.


This paper mainly solved the problem of predicting the time required for vehicles to pass through congested roads. In order to obtain more accurate prediction time, a Kalman prediction model based on multiple linear regression was established in this paper. Taking the 2008 Yanan elevated road in Shanghai as an example, the measured data in this section was collected from the traffic measured data sharing network, and the above model was used to obtain good prediction results. As an improvement, we used BP neural network instead of multiple linear regression to make the prediction result more in line with the actual situation.


Keywords: Multiple linear regression equation; Kalman filter algorithm; BP neural network.

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How to cite this article:
Tianlong Wang, Xiaorui Tao, Jiamei Zhang, Yulei Li. Predicting Traffic Congestion Time Based on Kalman Filter Algorithm. Advances in Research and Reviews, 2020; 1:7. DOI: 10.28933/arr-2020-06-2205


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