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.
Highlights
Traffic jam is a common problem of urban traffic
Et al [25], used the mixed wavelet packet method to remove the noise in the traffic flow. This is not a good solution because once the traffic flow changes, caused by abnormal conditions which occur in practical application, the existing traffic flow prediction system will impair the accuracy, which will indirectly lead to the failure of the traffic control system
The Bayesian network traffic flow short-term prediction method based on quantile regression is a real-time online prediction method
Summary
Traffic jam is a common problem of urban traffic. Advanced and efficient traffic signal control strategy is a low-cost and efficacious way to alleviate traffic congestion. Jiang XM et al [25], used the mixed wavelet packet method to remove the noise in the traffic flow This is not a good solution because once the traffic flow changes, caused by abnormal conditions which occur in practical application, the existing traffic flow prediction system will impair the accuracy, which will indirectly lead to the failure of the traffic control system. The method proposed in this paper considers the characteristics of traffic flow spatiotemporal correlation, and has good prediction accuracy when the traffic flow in the road network changes abnormally. This prediction method is not sensitive to partial data loss. If traffic flow prediction is carried out in the case of partial loss of real-time traffic flow data, the accuracy of prediction will be compromised to some extent, a relatively accurate prediction result can still be obtained
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