To better grasp the status quo of urban rail transit (URT) safety research and explore the knowledge base and hotspot trends, 1249 URT safety research papers from 1983-2018 were collected from the SCIE and SSCI databases of the WOS search platform as data samples. Co-occurrence analysis, burst analysis of keywords and co-citation analysis were adopted to analyze the current research situation of URT safety in the world by means of information visualization. The study found that risk analysis in the construction/operational phase, methods for predicting ground movements, and cause analyses of tunnel deformation and settlement are the knowledge bases in the field of URT safety research. Tunneling and Underground Space Technology, Safety Science, Atmospheric Environment, Lancet and Transportation Research Record are the core journals in the field. At present, the basic theory and research system of URT safety have been basically completed, but the research directions are too concentrated, and the frontier branches are too few. The research themes of URT safety were roughly grouped into three core paths: the "health risk" path, the "risk analysis — safety management" path and the "tunnel displacement/deformation" path. In the process of research and evolution, the research paths are refined step-by-step, and the research directions are transformed from macro to micro. The emergency evacuation of personnel in subway stations, the effects of particulate matter in subway air on human health and the use of computer technology for engineering optimization are the current frontiers of URT safety. URT safety research also tends toward risk analysis in the operational phase.

It is of great significance for coal mining and utilization to study the adsorption process of mixed gas in coal. In this paper, the Monte Carlo method (MC) is employed to study the competitive saturation adsorption of oxygen and water vapor inside coal particles, and then the convection, diffusion and adsorption inside and between particles are studied by lattice Boltzmann method (LBM). In addition, this study examines the impacts of porosity, average particle size, and gas concentration on the process of adsorption in coal porous media. The research results show that oxygen and water vapor present in the mixed gas experience increased permeability, diffusion rate, and saturated adsorption capacity as the porosity and average particle size of the coal porous medium increase. However, the time required to achieve saturated adsorption decreases. Under the condition of maintaining the proportion of gas components and altering the initial gas concentrations from 4.087 to 53.131 mol/m³, saturated adsorption capacity of both gases remains nearly unchanged. Yet, the effective diffusivity of gases declines with increasing initial concentration. Additionally, it is also found that water vapor diffuses more quickly than oxygen in the mixed gas and achieves adsorption saturation faster.

After a mine-fire accident occurs, a large number of emergency resources need to be allocated to rescue those involved in the mine-fire accident. The allocation of emergency resources for mine-fire accidents has the characteristic of being a complex system with strong uncertainty. To investigate the impact of various variables on the allocation of emergency resources in mine-fire situations, this paper analyzes the relevant factors that influence the process of allocating emergency resources during mine fires. It defines the variables of the mine-fire emergency resource-allocation system based on relevant assumptions. Causal loop and stock flow diagrams are drawn to illustrate the relationships between the variables and the system dynamics equation. Finally, a system dynamics model for mine-fire emergency resource allocation is established. The Vensim software was used to simulate the model of a mine-fire emergency rescue. The simulation produced curves for the evolution rate of the fire, the arrival rate, the demand for emergency resources, in-transit resources, arrival, and the usage of resources during the emergency. The results indicate a positive correlation between the quantity of emergency resources in-transit and the arrival rate of emergency resources: they are positively correlated with the amount of emergency-management investment. Additionally, the duration of the maximum quantity of emergency resources in-transit is positively correlated with the length of the emergency resource-allocation route. On the other hand, the evolution rate of the mine fire and the arrival rate of its emergency resources are negatively correlated with the level of emergency management. The evolution rate of the mine fire becomes larger and the damage caused by the mine-fire accident is greater when the decision-making ability of commanders is at a low level.

The purpose of this study is to accurately calculate the escape velocity of miners under different roadway conditions during mine fire periods. The experiment to examine escape velocity was carried out in the roadway of the Mu Chengjian Coal Mine. In the experiment, the relationships between the miner escape velocity and the inclination of the roadway, the area of the accessible section of the roadway, and the average wind velocity of the roadway were obtained through single factor analysis, and two multiple regression equations of the escape velocity were developed through multivariable linear regression analysis. The escape velocities of the miners were then predicted, and the error was analyzed with multiple regression equations. The experimental results show that the miner escape velocity decreases with an increase in the absolute value of the inclination of the roadway and increases with an increase of the accessible section area of the roadway and the average wind velocity of the roadway. In addition, the multiple regression equations have the strongest significance if the independent variables are the inclination of the roadway, the accessible section area of the roadway, and the type of roadway, and the contribution rate of the inclination of the roadway to the escape velocity is the highest. The predicted results calculated by the multiple regression equations are close to the experimental data, and the prediction errors are less than 10%. Consequently, we conclude that the multiple regression equations can be used to predict the miner escape velocity during periods of mine fires.