In recent years, there are more and more applications of traffic violation monitoring in some countries. The present work aims to analyze the vehicle speeds nearby road traffic violation monitoring area on urban main roads and find out the impact of road traffic violation monitoring on the vehicle speeds. A representative urban main road section was selected and the traffic flow was recorded by camera method. The vehicle speeds before, within, and after the road traffic violation monitoring area were obtained by the calculation method. The speed data was classified and processed by SPSS software and mathematical method to establish the vehicle speed probability density models before, within, and after the road traffic violation monitoring area. The results show that the average speed and maximum speed within the traffic violation monitoring area are significantly slower than those before and after the traffic violation monitoring area. 70.1% of the vehicles before the road traffic violation monitoring area were speeding, and 80.2% of the vehicles after the road traffic violation monitoring area were speeding, while within the road traffic violation monitoring area, the speeding vehicles were reduced to 15.9%. When vehicles pass through the road traffic violation monitoring area, the vehicle speeds tend to first decrease and subsequently increase. In its active area, road traffic violation monitoring can effectively regulate driving behaviors and reduce speeding, but this effect is limited to the vicinity of the traffic violation monitoring. The distribution of vehicle speeds can be calculated from vehicle speed probability density models.

To study the influence of illumination and longitudinal slope at the entrance and exit of an undersea tunnel on driver EEG characteristics, a real vehicle experiment was performed with the Jiaozhou Bay Undersea Tunnel. The experimental data of a driver’s real vehicle experiment were collected using an illuminance meter, EEG instrument, video recorder and other experimental equipment. The EEG power spectrum was classified according to frequency, the difference between the EEG power spectrum at the entrance and exit sections and other regions was analyzed, and the influence of the illumination and longitudinal slope of the undersea tunnel on the brain activity of drivers was studied. The region near the entrance and exit of the undersea tunnel was divided equidistantly, the changes in the EEG power of the driver during the process of entering and exiting the undersea tunnel were analyzed, and the changes in brain activity and different brain regions during the process were studied. Based on the EEG power, the model of illumination, longitudinal slope and their coupling effect was established. The traffic safety of the entrance and exit of the undersea tunnel was analyzed, and a high-risk driving region was found. The results show that the power spectrum of the entrance and exit sections of the undersea tunnel is obviously different from those of other sections. At 50 m behind the entrance point and 50 m in front of the exit point of the undersea tunnel, the power of the β wave changes rapidly and is at a high level. The consistency between the variation law of the β wave and the variation law of illumination is high. At the entrance and exit of the undersea tunnel, the active regions of the driver’s brain are concentrated in the frontal lobe and occipital lobe.

To study the influence of the driving environment of an undersea tunnel on driver EEG (electroencephalography) characteristics and driving safety, a real vehicle experiment was performed in the Qingdao Jiaozhou Bay Tunnel. The experimental data of the drivers’ real vehicle experiment were collected using an illuminance meter, EEG instrument, video recorder and other experimental equipment. The undersea tunnel is divided into different areas, and the distribution law of driving environment characteristics, EEG characteristics and vehicle speed characteristics is analyzed. The correlations between the driving environment characteristics, EEG characteristics and vehicle speed characteristics model the variables that pass the correlation test. The driving safety evaluation model of an undersea tunnel is established, and the driving safety in different areas of the undersea tunnel is evaluated. The results show that there are obvious differences in illumination, EEG power change rate, vehicle speed and other variables in different areas of the undersea tunnel. The driving environment characteristics are highly correlated with the β wave power change rate. The driving safety of different areas of the undersea tunnel from high to low is: upslope area, downslope area, exit area and entrance area. The study will provide a theoretical basis for the safe operation of the undersea tunnel.

At present, there is no standard or research to define the specific length of the entrance and exit sections of an undersea tunnel. In combination with the unique longitudinal slope of the undersea tunnel and the change in the illumination difference, the lengths of the lane change, transition and adaptation sections of the entrance and exit sections of the undersea tunnel were theoretically deduced, and a model for the entrance and exit sections lengths was established. Considering the Qingdao Jiaozhou Bay undersea tunnel as an experimental subject, the accuracy of the model of the entrance and exit section lengths was verified using two methods. The results indicated that the lengths of the entrance and exit sections of undersea tunnels change with changes in the factors such as the illumination, vehicle speed, and slope. In the case of the Qingdao Jiaozhou Bay undersea tunnel, when the vehicle passes the undersea tunnel at a speed of 70 km/h on a sunny day, according to the studied model, the entrance and exit sections of the undersea tunnel are 146.7 m and 157.1 m long, respectively. The entrance and exit section lengths of the undersea tunnel, determined using test method 1 and test method 2 are 151.0m, 153.6m and 161.0m, 148.9m, respectively. The absolute errors between the results of the model and test method 1 and 2 are less than 10%. These findings indicate that the model for the entrance and exit section lengths of undersea tunnels is reasonably accurate.