Expressway real-time traffic accident risk assessment method based on deep learning

Abstract

The invention discloses a highway real-time traffic accident risk assessment method based on deep learning, and the method comprises the steps: firstly dividing a highway into road segments through employing the basic information of an ETC portal, highway intercommunication and a toll station, and building the upstream and downstream association relation between the road segments; calculating thetraffic flow, the traffic flow speed and the traffic flow density of each road section respectively, obtaining road information, meteorological information and accident information, converting the road information, the meteorological information and the accident information into one-hot codes, and then performing data fusion, data resampling and standardization on four types of information corresponding to upstream and downstream road sections of an accident occurrence point; distinguishing time sequence features and non-time sequence features according to the acquired data, and constructing and training a deep learning model; and finally, according to the trained deep learning model, carrying out real-time evaluation on the risk level of the traffic accident of each road section of the expressway, and calculating to obtain an accident risk level index. According to the invention, the highway traffic accident risk level can be evaluated timely and accurately.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a deep learning-based real-time traffic accident risk assessment method for expressways. Background technique [0002] As the backbone road connecting important cities and regions, expressway plays an irreplaceable and important role in road transportation. Traffic accidents are the root cause of road congestion and reduced road traffic efficiency and safety in expressways. How to reduce the number of traffic accidents is a major safety challenge faced by global expressway managers. Real-time and rapid assessment of the traffic accident risk of each road can help prevent traffic accidents, improve road safety, and improve road traffic efficiency. Currently, highway accident risk assessment usually uses manual assessment. However, real-time evaluation of different road sections cannot be achieved by using manual evaluation. Manual evaluation has a long time de...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the characteristics of the expressway and the deficiencies of the prior art, the present invention proposes a real-time traffic accident risk assessment method for the expressway based on deep learning, aiming to solve the problem that the risk assessment of expressway traffic accidents in the prior art is usually manually assessed, and the assessment results Not timely, inaccurate, and unable to realize the technical problem of real-time traffic accident risk assessment of the whole road section

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