Method for quantifying behavior risk of driver in highway tunnel environment

Abstract

The invention belongs to the technical field of traffic safety, and discloses a method for quantifying a behavior risk of a driver in a highway tunnel environment. The method comprises the steps: collecting the real-time speeds of corresponding speed sampling points when each driver passes through a tunnel entrance segment, a tunnel travelling segment, a tunnel exit segment and a common highway segment, and enabling the real-time speeds to serve as the subjective expected vehicle speeds of the drivers; obtaining the vehicle speed of each driver at a 50 position of each segment; calculating the highest and lowest safety vehicle speeds of each segment; obtaining an objective safety vehicle speed according to the highest and lowest safety vehicle speeds of each segment; obtaining the safety vehicle speed difference of each driver at the corresponding speed sampling points of each segment according to the subjective expected vehicle speed and objective safety vehicle speed of each driver at the corresponding speed sampling point of each segment, and enabling the safety vehicle speed difference to serve as a behavior risk quantification index of the corresponding driver, wherein the larger safety vehicle speed difference at the corresponding speed sampling point indicates the higher behavior risk of the driver at the speed sampling point of the corresponding segment.

Description

technical field [0001] The invention belongs to the technical field of road traffic safety, and in particular relates to a behavior risk quantification method of a driver in an expressway tunnel environment. Background technique [0002] As tunnel construction accounts for an increasing proportion of expressway infrastructure, the accompanying traffic safety issues have become increasingly prominent. Accident data show that the traffic accidents in the entrance and exit sections of the tunnel are the most distributed, accounting for nearly 70% of the total, and rear-end collision accidents account for nearly 55%. The severity of the accidents is significantly higher than that in the open road section. Among the many factors affecting traffic safety, the driver factor accounts for about 93% of the total number of accidents. Due to the special geometric space structure of the tunnel and the obvious difference between the internal and external lighting environments, the driver...

AI Technical Summary

Problems solved by technology

[0003] Existing research on tunnel safety focuses on the analysis of road static elements based on empirical statistics, neglects the dynamic characteristics of driving behavior, and rarely pays attention to the influence of drivers’ subjective cognition, even considering the influence of drivers’ risk perception characteristics , but did not conduct real vehicle experiments, choose appropriate behavior indicators for in-depth analysis

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