Skip to the main content

Original scientific paper

https://doi.org/10.7307/ptt.v32i6.3394

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

Jing Luo orcid id orcid.org/0000-0003-2429-4608 ; Wuhan University of Technology


Full text: english pdf 1.007 Kb

versions

page 821-835

downloads: 232

cite


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.

Keywords

traffic flow prediction; urban road network; spatiotemporal correlation; quantile regression; Bayesian network

Hrčak ID:

253160

URI

https://hrcak.srce.hr/253160

Publication date:

18.11.2020.

Visits: 599 *