Expressway accident risk real-time prediction method based on road section heterogeneity

Abstract

The invention relates to an expressway accident risk real-time prediction method based on road section heterogeneity, and the method specifically comprises the following steps: S1, obtaining the roadgeometric data of a target expressway, dividing the corresponding road section types, obtaining the historical accident data, and extracting the accident occurrence time; S2, according to the road section types and the accident occurrence time, calculating to obtain a main source of the expressway accident risk by combining a significance test and a random forest algorithm; S3, establishing an accident risk prediction model based on the main source of the expressway accident risk, obtaining traffic flow data and weather data in real time, inputting the traffic flow data and the weather data into the accident risk prediction model in combination with road geometric data, and outputting an accident risk prediction result; and S4, judging whether traffic control measures need to be taken or not according to the accident risk prediction result, if so, implementing a corresponding traffic control strategy, and otherwise, returning to the step S3. Compared with the prior art, the method hasthe advantages of being good in real-time performance, improving the accuracy and transportability of the accident risk prediction model and the like.

Description

Technical field[0001]The invention relates to the technical field of expressway traffic safety prediction, in particular to a method for real-time prediction of an expressway accident risk based on the heterogeneity of road sections.Background technique[0002]Urban expressways are an important part of the modern transportation system. Once a traffic accident occurs, it is very easy to induce long-distance traffic congestion and cause large-scale travel delays. Among various measures to reduce the frequency of accidents on expressways, active safety control is the key, and accurate accident risk prediction is the prerequisite for active safety control. The accident risk prediction result can be used as the input of active safety management and control, so as to determine the type and level of active safety management and control measures.[0003]The real-time prediction technology of accident risk has developed rapidly in recent years and has achieved gratifying results, but there are s...

AI Technical Summary

Problems solved by technology

[0004] 1. Existing prediction technologies do not consider the differences in accident mechanisms between road types, or develop prediction models for specific road types, resulting in low prediction accuracy and poor portability;

[0005] 2. The spatio-temporal dimension of the input data in the existing forecasting technology cannot meet the real-time management and control needs of networked vehicles. This field urgently needs the emergence of risk forecasting technology with fast update frequency;

[0006] 3. The existing prediction technology cannot reflect the sameness and difference in the accident mechanism between the types of road sections

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