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


1. Zhenchao Hou. Prediction of bus journey time based on the combined model of Kalman filter and exponential smoothing method [D]. Liaoning university,2018.
2. Yuxin Long. Mathematical model of urban highway traffic jam warning [J]. Science and technology economics guide,2018,26(03):183.
3. Moggu Xu. Prediction and evaluation of traffic congestion on urban trunk roads under accident conditions [D]. Chang ‘an university,2017.
4. Siqi Yang. Research and application of expressway traffic congestion prediction model [D]. South China university of technology,2017.
5. Chunjiao Dong, Chunfu Shao, Xuemei Zhou, Meng Meng, Gechengxiang Zhu. Short-time prediction Kalman filter algorithm based on traffic flow parameters [J]. Journal of southeast university (natural science edition),2014,44(02):413-419.
6. Wei Nie. Study on characteristics of expressway traffic accidents and induced control strategies [D]. Southwest Jiaotong University,2012.
7. Chunhui Zhang, Rui Song, Yang sun. Short-term
passenger flow prediction of bus stations based on Kalman filter [J]. Transportation system engineering and information,2011,11(04):154-159.
8. Zhong Zhu, Zhaosheng Yang. Real-time travel time prediction model based on Kalman filter theory [J]. Theory and practice of systems engineering,1999(09):74-78.