To improve the driving safety of high-altitude highway tunnel entrance sections in a fog environment, the physiological changes of drivers during driving in the tunnel entrance section were studied. Firstly, the hypoxia environment was constructed, and the real vehicle test was carried out under different foggy conditions, and the test data such as vehicle speed and heart rate were collected. Secondly, the single factor analysis method was used to test the significant difference in the data, analyze the influence law of tunnel entrance velocity on heart rate. Finally, establish the relationship model between velocity and heart rate. The results show that with the increase in fog concentration, the vehicle speed decreases, and the heart rate increases. The vehicle speed in the dense fog environment was significantly lower than that in the other three fog environments. The heart rate in the dense fog environment was significantly higher than that in the other three fog environments, and the maximum heart rate in the four environments all appeared within the range of 50 m from the tunnel entrance to the tunnel entrance. The relationship model showed that there was a strong correlation between speed and heart rate, and excessive speed would lead to increased heart rate and enhanced physiological discomfort for drivers. The existence of a low oxygen environment leads to a higher heart rate in drivers, which is not conducive to driving safety.
Most of the fixed traffic detectors on urban roads are mainly based on one type of detector layout. For a specific area, the detection accuracy and layout cost of the detector layout in this way cannot meet the requirements of this road section. Therefore, according to the NSGA-II multi-objective algorithm rule, to accurately collect urban road traffic flow information and the lowest cost of coordinated deployment of traffic flow detectors, a mathematical model based on the accuracy of traffic flow detection and the lowest deployment cost is created. The type and quantity of λ are variables, and the NSGA-II multi-objective algorithm is used to obtain the type and an optimal number of detectors for co-optimization of detectors in a specific area. Compared with the traffic flow detection accuracy of a single detector in a specific area and the deployment cost that meets the detection conditions, the coordinated deployment and collaborative detection of multiple detectors not only significantly improves the traffic flow detection accuracy in this area, but also reduces the deployment cost.
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