Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

Keywords: traffic flow prediction, urban road network, spatiotemporal correlation, quantile regression, Bayesian network

Abstract

With the popularization of intelligent transportation system and Internet of vehicles, the traffic flow data on the urban road network can be more easily obtained in large quantities. This provides data support for shortterm traffic flow prediction based on real-time data. Of all the challenges and difficulties faced in the research of short-term traffic flow prediction, this paper intends to address two: one is the difficulty of short-term traffic flow prediction caused by spatiotemporal correlation of traffic flow changes between upstream and downstream intersections; the other is the influence of deviation of traffic flow caused by abnormal conditions on short-term traffic flow prediction. This paper proposes a Bayesian network short-term traffic flow prediction method based on quantile regression. By this method the trouble caused by spatiotemporal correlation of traffic flow prediction could be effectively and efficiently solved. At the same time, the prediction of traffic flow change under abnormal conditions has higher accuracy.

Author Biography

Jing Luo, Wuhan University of Technology

Wuhan University of Technology, School of Transportation

 

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Published
2020-11-11
How to Cite
1.
Luo J. Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression. PROMET [Internet]. 2020Nov.11 [cited 2020Nov.29];32(6):821-35. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/3394
Section
Articles