The optimization of passenger evacuation path in subway station under emergencies directly affects the evacuation efficiency. Aiming at the evacuation scenario of passengers in subway station under multiple fires, a path planning method based on multi-objective robust optimization is established in this paper. The shortest total evacuation time, the minimum total risk and the minimum total congestion cost are taken as the objectives where the robustness of travel time and risk is also considered. NSGA-II algorithm is used to solve the model, the optimal Pareto solution under a certain robust control parameter is obtained according to the principle of minimizing the total cost function, and the overall optimization degree of route is obtained by using the evaluation index. Taking Qingdao May 4th Square subway station of China as a verification example, a fire diffusion model is built by using Pyrosim, and a passenger evacuation system is built by using Pathfinder. The multi-objective robust path optimization model of passenger evacuation is applied to the built passenger evacuation system under fires. The simulation results show that the overall optimization degree of route can reach 12.8 $\%$ , when the time and risk robust control parameters are 30 and 30, respectively. The method can be used to guide the passenger evacuation in subway stations under multiple hazard sources and improve the safety index of passenger travel.

To identify electrical vehicle (EV) distribution paths with high robustness, insensitivity to uncertainty factors, and detailed road-by-road schemes, optimization of the distribution path problem of EV with multiple distribution centers and considering the charging facilities is necessary. With the minimum transport time as the goal, a robust optimization model of EV distribution path with adjustable robustness is established based on Bertsimas’ theory of robust discrete optimization. An enhanced three-segment genetic algorithm is also developed to solve the model, such that the optimal distribution scheme initially contains all road-by-road path data using the three-segment mixed coding and decoding method. During genetic manipulation, different interlacing and mutation operations are carried out on different chromosomes, while, during population evolution, the infeasible solution is naturally avoided. A part of the road network of Xifeng District in Qingyang City is taken as an example to test the model and the algorithm in this study, and the concrete transportation paths are utilized in the final distribution scheme. Therefore, more robust EV distribution paths with multiple distribution centers can be obtained using the robust optimization model.

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.

A large number of traffic tunnel accidents have been reported in China since the 21th century. However, few studies have been reported to analyze traffic accidents that have occurred in urban road tunnels. This study aims to examine the characteristics of the temporal, spatial, and modality distributions of traffic in Shanghai river crossing tunnels using statistical analysis and comparative analysis. Employing these techniques tunnel accident data obtained from Shanghai center 110 was analyzed to determine temporal and spatial distribution characteristics of traffic accidents in river crossing tunnels in Shanghai. The results of this analysis are discussed and summarized in this paper. Identification of the characteristics of tunnel traffic accidents can provide valuable information for development of effective countermeasures to improve tunnel safety in China.

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.