With the flourishing development of the urban metro system, the topology of important nodes changes as the metro network structure evolves further. The identical important node has distinct impacts on various metro networks’ resilience. At present, the dynamic influences of important station evolution on the resilience of metro networks remain to be studied further. Taking Shenzhen Metro Network (SZMN) as an example, the dynamic influences of the structure evolution of important nodes on the resilience of the metro network were investigated in this study. Firstly, the dynamic evolution characteristics of complex network topology and node centralities in metro systems were mined. Then, combined with the node interruption simulation and the resilience loss triangle theory, the resilience levels of distinct metro networks facing the failure of the same critical node were statistically assessed. Additionally, suggestions for optimal network recovery strategies for diverse cases were made. Finally, based on the evaluation results of node importance and network resilience, the dynamic influences of the topological evolution of important nodes on the resilience of metro networks were thoroughly discussed. The study’s findings help us comprehend the metro network’s development features better and can assist the metro management department in making knowledgeable decisions and taking appropriate action in an emergency. This study has theoretical and practical significance for the resilient operation and sustainable planning of urban metro network systems.

In the context of spatio-temporal big data, the complexity of urban metro network is highlighted. For the safe operation and resilient management of urban rail transit networks, it is advantageous to correctly comprehend the complex topological dynamics characteristics of the weighted metro network based on the massive mobility of passenger flow. In this study, the weighted Shenzhen Metro networks (WSZMNs) in the morning and evening rush hours were modeled based on Space L model and spatio-temporal big data of cross-sectional passenger flow. Combined with six complex indicators, the topological complexity of WSZMNs in two periods was compared based on quantitative and geographical distributions. Based on the multi-attribute decision making method, the weighted comprehensive importance of all nodes in morning and evening rush hours was also quantitatively evaluated and geographically visualized. Results indicate that the WSZMN exhibited some geographical heterogeneity, and the complexity of WSZMN in the morning rush hours was more prominent than in the evening rush hours. Additionally, for the network's critical stations, essential locations, and significant periods, there was often large-scale and massive mobility of passenger flow. The metro operation management department should strengthen the targeted passenger flow control to improve the safety and resilience of Shenzhen Metro network. The relevant research findings help us get a better understanding of the complexity of metro network system under the massive passenger flow mobility in the rush hours. This study can provide specific theoretical and practical references for the urban smart metro operation department to manage the massive mobility better.

With the prosperous development of the urban metro network, the characteristics of the topological structure and node importance are changing dynamically. Most studies focus on static comparisons, and dynamic evolution research is rarely conducted. It is necessary to track the dynamic evolution mechanism of the metro network from the perspective of development. In this paper, the Shenzhen Metro Network (SZMN) topology from 2004 to 2021 was first modeled in Space L. Five kinds of node centralities in eight periods were measured. Then, the dynamic evolution characteristics of the SZMN network topology and node centralities were compared. Finally, an improved multi-attribute decision-making method (MADM) was used to evaluate the node importance, and the spatiotemporal-evolution mechanism of the node importance was discussed qualitatively and quantitatively. The results show that, with the spatiotemporal evolution of the SZMN, the nodes became more and more intensive, and the network tended to be assortative. The different kinds of node centralities changed variously over time. Moreover, the node importance of the SZMN gradually dispersed from the core area of Chegongmiao–Futian to the direction of the Airport and Shenzhen North. The node importance evolves dynamically over time, and it is closely related to the changes in the node type, surrounding nodes and whole network environment. This study reveals the dynamic evolution mechanism of the complex topology and node importance in the SZMN, which can provide scientific suggestions and decision support for the planning, construction, operation management and resilient sustainable development of the urban metro.

An urban metro network is susceptible to becoming vulnerable and difficult to recover quickly in the face of an unexpected attack on account of the system's complexity and the threat of various emergencies. Therefore, it is necessary to assess the resilience of urban metro networks. However, the research on resilience assessment of urban metro networks is still in the development stage, and it is better to conduct said research using a technique which combines many attributes, multiple methods, and several cases. Therefore, based on the complex network modeling and topological characteristics analysis of metro systems, a metro network's robustness and vulnerability measurement method under node interruption and edge failure is proposed for the first time in this study. Then, considering the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, a quantitative resilience assessment model of metro networks is put forward, and the corresponding recovery strategies are discussed. Finally, a case study of the Zhengzhou Metro Network (ZZMN) under an extreme rainstorm is conducted to demonstrate the viability of the proposed model. The results show that ZZMN possesses scale-free and small-world network properties, and it is robust to random interruptions but vulnerable to deliberate attacks. ZZMN still needs to improve its effectiveness in information transmission. The centrality distribution for each node in the ZZMN network differs significantly, and each node's failure has a unique impact on the network. The larger the DC, BC, and PR of a node is, the lower the network's robustness after its removal is, and the stronger the vulnerability is. Compared with the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, the network loss caused by tunnel failure was the lowest, followed by general station interruption, and the interruption at interchange stations was the most costly. Given the failures under various cases, the metro management department should prioritize selecting the optimal recovery strategy to improve the resilience of the metro network system. This study's findings can assist in making urban metro systems less vulnerable to emergencies and more resilient for a quick recovery, which can provide scientific theoretical guidance and decision support for the safety and resilient, sustainable development of urban metro systems.

Based on gas adsorption theory, high-pressure mercury intrusion (HPMI), low-temperature liquid nitrogen gas adsorption (LT-N₂GA), CO₂ adsorption, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and small-angle X-ray scattering (SAXS) techniques were used to analyze the pore structures of six coal samples with different metamorphisms in terms of pore volume, specific surface area (SSA), pore size distribution (PSD) and pore shape. Combined with the gas adsorption constant a, the influence and mechanism of the pore structure of different coal ranks on gas adsorption capacity were analyzed. The results show that there are obvious differences in the pore structure of coals with different ranks, which leads to different adsorption capacities. To a large extent, the pore shapes observed by SEM are consistent with the LT-N₂GA isotherm analysis. The pore morphology of coal samples with different ranks is very different, indicating the heterogeneity among the coal surfaces. Adsorption analysis revealed that mesopore size distributions are multimodal and that the pore volume is mainly composed of mesopores of 2-15 nm. The adsorption capacity of the coal body micropores depends on the 0.6-0.9 nm and 1.5-2.0 nm aperture sections. The influence of coal rank on gas desorption and diffusion is mainly related to the difference in pore structure. The medium metamorphic coal sample spectra show that the number of peaks in the high-wavenumber segment is small and that it is greater in the high metamorphic coal. The absorption intensity of the C-H stretching vibration peak of naphthenic or aliphatic hydrocarbons varies significantly among the coal samples. Over a small range of angles, as the scattering angle increases, the scattering intensity of each coal sample gradually decreases, and as the degree of metamorphism increases, the scattering intensity gradually increases. That is, the degree of metamorphism of coal samples is directly proportional to the scattering intensity. The influence of coal rank on gas adsorption capacity is mainly related to the difference in pore structure. The gas adsorption capacity shows an asymmetric U-shaped relationship with coal rank. For higher rank coals (Vdaf < 15%), the gas adsorption consistently decreases significantly with increasing Vdaf. In the middle and low rank coal stages (Vdaf > 15%), it increases slowly with the increase of Vdaf. We believe that the results of this study will provide a theoretical basis and practical reference value for effectively evaluating coal-rock gas storage capacity, revealing the law of CBM enrichment and the development and utilization of CBM resources.

The performance-based approach using Required Safety Egress Time / Available Safety Egress Time concept is the most common method in fire safety evaluation. However, this one-dimension approach cannot fully evaluate the performance of a complex scenario with thousands of occupants in subway stations. Therefore, this paper develops a four-dimension parameter system that includes Required Safety Egress Time, Average Evacuation Time, Average Waiting Time, and Average Moving Distance to quantify the evacuation performance from four aspects. The convergence test approach is adopted to validate the accuracy of simulation results. A dimensionless parameter Risk Index (RI) is proposed to support multi-dimensional risk evaluation and comparison. One realistic standard subway station in Guangzhou metro system is modeled to testify the applicability of the proposed method. Totally twelve evacuation scenarios are designed and simulated, results indicate that: 1) Repeated simulations are necessary. Up to 629 simulation run times are required to meet the acceptance criterion of 0.01% for mean and 1% for standard deviation. 2) The Risk Index of Average Waiting Time is distinctly higher than other Risk Indexes. A long waiting time at bottlenecks increases the risk level in all scenarios. 3) The risk evaluation result of comprehensive RI is consistent with but not the same as the evaluation conclusion of RSET. The difference between RI and RSET shows that RI integrates more aspects of evacuation risk than RSET. The method proposed could support safety evaluation capacity improvement and standard revision in metro system.

Extreme rainfall events, such as heavy rainfall and typhoon, can cause unexpected disruptions to the metro ridership and operating system, resulting in severe consequences such as infrastructure malfunctions, service termination and system paralysis. This paper focuses on the spatio-temporal impacts and resilience assessment of extreme rainfall events on metro ridership. The ridership data used in this paper are from the Automatic Fare Collection (AFC) system in Shenzhen Metro, and the time ranges from May to September in 2017 and 2018 with the 15-minute granularity. This paper not only utilizes big data to analyze the spatio-temporal characteristics of passenger flow under heavy rainfall and typhoon, but also innovatively introduces the meteorological warning signals and ridership resilience curve to analyze the resilience of ridership. The main results reveal that the general heavy rainfall affects passenger flow in the spatio-temporal imbalance. Especially for the spatial aspect, the imbalance of direction and section in peak hours significantly aggravates and the section passenger volume is even larger than usual. For typhoon events, extreme weather can strongly affect the distributions and recovery of metro ridership. Stronger typhoons can have a greater impact on resilience, but continuous rainfall can lead to a longer recovery time. The study results can help metro management agencies better understand the impacts of extreme weather on metro ridership to build a more weather-resilience metro system. (C) 2021 Elsevier B.V. All rights reserved.

With the development of complex networks in urban rail transit (URT), the topological structure changes accordingly and node importance also redistributes dynamically. However, many deficiencies exist in the single measure or unweighted network or static network when ranking node importance. Most importantly, the evolution mechanism of node importance with the network development is seldom studied. In view of this, in this paper, six unweighted and weighted complex networks are firstly modeled in the evolution of URT networks. One of Multiple Attribute Decision Making (MADM) methods is proposed, that is WTOPSIS (The Weighted Technique for Order of Preference by Similarity to Ideal Solution) algorithm combining Coefficient of Variation method and TOPSIS. Then four local and global centralities are aggregated and utilized in WTOPSIS to rank the node importance in those six networks. On the basis, the intersection degrees among the ranking sets are calculated to evaluate the similarities of ranking results. Furthermore, the factors contributing to the evolution of node importance are discussed quantitatively and qualitatively with examples. Finally, the feasibility of the method is verified by the Shenzhen Metro system in 2016. Results show that WTOPSIS algorithm outperforms the single attribute in ranking node importance, which makes up for the shortcomings in existing studies. Besides, for different stations in URT network development, node importance evolution is affected differently by the changes of topological structure and passenger flow. It is necessary to combine with the actual situations for the specific analysis. This study reveals the evolution mechanism of the node importance in the development of URT networks and it also has great theoretical and practical significance.

To study the topological complexity of urban rail transit (URT) networks with the multi-line transfer stations from different perspectives, Shenzhen Metro (SZM) is taken as an example and Space L & Space P models are established in this study. Then, based on multiple evaluation parameters and key nodes ranking, the differences of network topological complexity in two models are deeply explored and compared quantitatively. Some meaningful results have been obtained: (i) The characteristics of scale-free networks in Space L and Space P are proved through the eigenvector centrality distribution and truncated power-law distribution of cumulative degree. Scale-free networks show both robustness against random faults and vulnerability against deliberate attacks. The daily safety management at 16.87% of hub stations in Space P and 17.47% of hub stations in Space L should be taken seriously by metro managers in case of emergency events. (ii) Since the WS small-world effect in Space P model is more evident than that in Space L model, the connections among stations and OD accessibility of passenger are enhanced in Space P network. (iii) The important and risk nodes are concentrated in Space L and are more decentralized in Space P. P model has the stronger overall anti-attack capability than L model, which is more beneficial to the resilience of network. This study can realize the deeper understanding of URT system with different models and it can provide theoretical support for complex network analysis of URT system.

Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city can be detected. This study proposes a data-mining model for understanding the patterns and regularities of human activities in urban areas from spatiotemporal datasets. This model uses a grid-based method to convert spatiotemporal point datasets into discretized temporal sequences. Time-series analysis technique dynamic time warping (DTW) is then used to describe the similarity between travel-demand sequences, while the clustering algorithm density-based spatial clustering of applications with noise (DBSCAN), based on modified DTW, is used to detect clusters among the travel-demand samples. Four typical patterns are found, including balanced and unbalanced cases. These findings can help to understand the land-use structure and commuting activities of a city. The results indicate that the grid-based model and time-series analysis model developed in this study can effectively uncover the spatiotemporal characteristics of travel demand from usage data in public transportation systems.

With the recent rapid development of cities, the dynamics of urban road-traffic commuting are becoming more and more complex. In this research, we study urban road-traffic commuting dynamics based on clustering analysis and a new proposed urban commuting electrostatics model. As a case study, we investigate the characteristics of urban road-traffic commuting dynamics during the morning rush hour in Beijing, China, using over 1.3 million Global Positioning System (GPS) data records of vehicle trajectories. The hotspot clusters are identified using clustering analysis, after which the urban commuting electric field is simulated based on an urban commuting electrostatics model. The results show that the areas with high electric field intensity tend to have slow traffic, and also that the vehicles in most areas tend to head in the same direction as the electric field. The results above verify the validity of the model, in that the electric field intensity can reflect the traffic pressure of an area, and that the direction of the electric field can reflect the traffic direction in that area. This new proposed urban commuting electrostatics model helps greatly in understanding urban road-traffic commuting dynamics and has broad applicability for the optimization of urban and traffic system planning.

With the rapid development of cities in recent years, the size of the cities is becoming bigger and bigger and the structure of the cities is becoming more and more complex. The first step to study the urban resilience is hotspots mining and POI analysis. This paper established an O-D hotspots clustering model based on Iterative Self Organizing Data Analysis Techniques Algorithm (hereinafter referred to as ISODATA) to mine the Origin-Destination (hereinafter referred to as O-D) hotspots in the rush hours in cities and study the distribution characteristics of Point of Interests (hereinafter referred to as POIs) in the hotspots area. It is found that the pick-up hotspots tend to be gathered in the residential zones and the drop-off hotspots tend to be gathered in the working zones. Besides, the distribution characteristics of POIs in both pick-up and drop-off hotspots areas and huge railway stations (special drop-off hotspots) areas are quite special. This study provides an in-depth understanding of the structure of the cities and provides an effective guidance in urban zones planning. This study also provides fundamental knowledge for urban resilience design.

In order to enhance Chinese workers' occupational safety awareness, it is essential to learn from developed countries' experiences. This article investigates thoroughly occupational safety and health (OSH) in China and the UK; moreover, the article performs a comparison of Chinese and British OSH training-related laws, regulations and education system. The following conclusions are drawn: China's work safety continues to improve, but there is still a large gap compared with the UK. In China a relatively complete vocational education and training (VET) system has been established. However, there exist some defects in OSH. In the UK, the employer will not only pay attention to employees' physiological health, but also to their mental health. The UK's VET is characterized by classification and grading management, which helps integrate OSH into the whole education system. China can learn from the UK in the development of policies, VET and OSH training.

Investigations on the effects of microscopic pore structure and coal composition on the re-sistivity could provide theoretical basis for the application of electrical prospecting in coal mine. There-fore, in this study, six groups of coal samples were selected and tested by means of low temperature liq-uid nitrogen adsorption (LT-LN2A), field emission scanning electron microscope (FE-SEM) and resis-tivity-measurement equipment. Then according to the conductive mechanism of coal, effects of micro-scopic pore structure and coal composition on the resistivity were studied. Results show that the effects of pore structure on coal resistivity are multivariate function relation with multiple factors. The porosity and specific surface area (SSA) of coal sample both show U-mode change with the volatile. Porosity and SSA of medium metamorphism coal are the lowest. And effect of metamorphism degree on porosity is greater than the SSA. Coal resistivity increases with the increase in the volatile while decreases with the increase in metamorphism degree. Meanwhile, a U-mode change also exists between the resistivity and porosity. The effect of micro-pores on resistivity is greater than that of macro-pores and mesopores. And more micro-pores could lead to lower resistivity. In contrast, the higher resistivity appears in coal with more macro-pores and mesopores due to the occurrence of cracks and fractures. In addition, the type and contents of mineral elements in coal keeps a positive correlation to the ion conductive function and a negative relationship with the resistivity.

Opening new lines will bring the structural change of passenger flow characteristics in network operation of Urban Rail Transit. Based on the smart card data from Shenzhen Metro, temporal-spatial characteristics was discussed comprehensively from different statistics dimensions and analysis angles. At the same time, passenger flow in 28 transfer stations was analyzed by clustering algorithm and the land use nature was attained. Consequently, the passenger flow law in Shenzhen Metro will be understood better combined with visualization. Especially, when facing the risk of large passenger flow, targeted measures can be taken in advance to improve the safety and resilience of Shenzhen Metro.

In order to explore the stage of Chinese coal production safety situation from the perspective of development,overall grasp the future development trend,and continuously improve the coal mine safety production level in China.The comparison method of the peer point and the peer change rate was used to compare the coal safety production situation of China and America from the perspective of historical development.The method of peer change rate and fitting exponential function prediction was used to forecast the future development trend and million tons mortality of Chinese coal mine.The results show that the current development level of Chinese coal mine safety production is close to that of the United States in 1970s,and the change rate is roughly the same as that of the United States from 1940s to 1950s;in future,the improvement speed of Chinese coal mine safety production level will gradually slow down,and the million tons mortality will be from 0.04 to 0.06 in 2020.

Coal is a kind of double medium composed of pore and fracture. The matrix pores mainly play a diffusion role in the process of gas migration in the coal seam and is the main storage space of coalbed methane. Nanoscale pore structure characteristics have an important effect on coalbed methane diffusion. In this paper, six groups of coal samples with different ranks are selected and tested by the means of low temperature liquid nitrogen adsorption (LT-LN2A) and FE-SEM to explore the nanoscale pore microstructure characteristics, respectively, from microscopic characterization or macro morphology. Combined with coal samples' CH4 desorption and diffusion experiments, the effects of the nanoscale pore structure characteristics on the coalbed methane diffusion are analyzed. Results show that for six coal samples with different ranks, the total pore volume has a good negative correlation to SSA and the average pore size, and a good positive correlation to the DA microporous aperture. The adsorption isotherm and SEM results of coal samples intuitively show the pore shape, connectivity and development degree of coal samples. CH4 desorption and diffusion experiments prove that the diffusion coefficient D changes with the attenuation trend over time, and the Kn comparison result indicated that the gas diffusion pattern is mainly Knudsen diffusion model in the micropores where the diffusion resistance is larger. On the contrary, the relatively larger pores are in conducive to the coalbed methane diffusion because the diffusion rate is higher and the diffusion coefficient D is larger. The diffusion rate and diffusion coefficient D have a good negative correlation to the microporous quantity proportion and have a good positive correlation to porosity. Research of the effects of nanoscale pore structure characteristics on coalbed methane diffusion can provide certain guidance and reference values for the effective evaluation of coal and rock gas' storage capacity and the revelation and prediction of coalbed methane enrichment regularity.

Coal is a kind of porous medium material and contains rich joint and complex pore structures. Spatial performance characteristics change with development patterns of pore structures and with this, different effects on acoustic velocity of coal samples will happen. In this paper, six groups of coal samples with different ranks are selected and tested using low temperature liquid nitrogen adsorption (LT-LN(2)A) and FE-SEM to study the nanoscale pore microstructure characteristics. Combined with acoustic wave velocity measurement, effects of pore structure on acoustic wave velocity of coal samples are analyzed. Results indicate that there is a negative correlation between Vdaf and acoustic wave velocity of coal samples with different ranks. Coal rank, specific surface areas (SSA), and total pore volume have a positive correlation to V-P and V-S of coal samples respectively. Both porosity and microporosity have a strong positive correlation to coal pore surface's development degree, and a good negative correlation to coal rank and acoustic wave velocity. Furthermore, correlation coefficient between coal samples' porosity and V-P increases from R-2 = 0.639 (effective porosity-V-P) to R-2 = 0.789 (microporosity-V-P), and correlation coefficient between the porosity and V-S increases from R-2 = 0.556 (effective porosity-V-S) to R-2 = 0.713 (microporosity-V-S), which indicates that the effect of coal samples' porosity on V-P is greater than V-S. Research can provide certain guidance and reference values for related data evaluation of geological acoustic logging and pore structure identification of coal samples.