Expressway traffic event detection method based on hybrid kernel correlation vector machine

Abstract

The invention discloses an expressway traffic event detection method based on a hybrid kernel correlation vector machine, and the method comprises the steps of constructing an expressway traffic event detection initial variable set according to the change characteristics of upstream and downstream traffic flow parameters of a traffic event; learning minority class sample information by adopting a conditional generative adversarial network, training a generator to generate minority class supplementary samples, and balancing data distribution; screening key variables out through variable importance measurement of an XGBoost algorithm; establishing a combined kernel function based on a local Gaussian kernel and a global polynomial kernel; taking the key variables as input, and training a multi-kernel relevance vector machine model; and optimizing parameters through an improved fruit fly optimization algorithm to obtain an optimal model. According to the invention, the traffic incident detection rate is improved, the traffic incident occurring on the expressway is detected in time, time is won for road emergency rescue, casualties and property loss in the event are reduced, and meanwhile, technical support is provided for road traffic safety risk early warning.

Description

technical field [0001] The invention belongs to the technical field of traffic safety, and in particular relates to a method for detecting traffic incidents on express roads. Background technique [0002] According to the statistics of the World Health Organization, about 1.3 million people die in road traffic accidents all over the world every year, and tens of millions of people are injured in varying degrees. Road traffic accidents have become a major public nuisance in human society, and traffic safety has also become a major social problem. Studies have shown that the severity of secondary accidents is much greater than that of primary accidents, and the risk is increased by 600% compared with primary accidents. The longer the incident processing time, the greater the probability of causing a secondary incident. Therefore, how to accurately and efficiently detect and identify road traffic incidents in the shortest possible time is the focus of traffic research. [00...

AI Technical Summary

Problems solved by technology

For example, the detection time of the detection model is long; the performance of the detection model is not good in the unbalanced data set; the detection rate of the detection model is low; on the other hand, there is also a lack of a general traffic incident detection framework system

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