Urban Accidents Research Articles

Estimating casualties from urban fires: A focus on building and urban environment information

This study developed two prediction models for urban fire occurrence and related casualties via a fire accident dataset from Seoul, South Korea, from 2017 to 2021. Our models exhibit improved predictive performance by incorporating built environment features, such as building characteristics and the urban context, alongside weather and demographic data. This approach showed improved predictive performance suitable for public health implementation. Compared with the weather- and demographic-only models, our models had an 18.1 % greater fire occurrence prediction accuracy and a 10.4 % greater casualty prediction accuracy. Major variables affecting fire occurrence include building characteristics, e.g., the floor area ratio (FAR), building age, and commercial building number. Important features affecting casualty occurrence include demographic aspects, e.g., income level and weather, and network-based features, e.g., road connectivity and fire station proximity. These findings suggest that fire prevention strategies and fire casualty prevention strategies may need to differ. Furthermore, we identify high-risk zones by conducting spatial analysis and fire risk and casualty prediction on all buildings by applying our models to Seoul's Gangnam District. These contributions can promote safe and healthy urban environments by improving fire risk prediction accuracy and providing important insights into urban planning for appropriate urban fire accident response and prevention.

Identify Affecting Factors on Total Fertility Rate: A Systematic Review

Background: The total fertility rate (TFR) is a key indicator of population dynamics and health. It shows the average number of children a woman would have in her lifetime, based on the current fertility rates for different age groups. Various factors affect the TFR, such as social, economic, political, technological, environmental, and health factors. By analyzing these factors and how they influence the TFR, policymakers and planners can design and implement interventions to achieve optimal population and health outcomes. Methods: We searched PubMed, Scopus, ISI Web of Science, and ProQuest databases, as well as the Google Scholar search engine systematically. We searched for studies from 13 December 2021 - 20 May 2022, without time limits. We included studies that described at least one driver of TFR. We applied the STEEPH framework (social, technology, environmental, economic, political, and health) method to analyze the qualitative data. Results: We included 64 studies from 26 countries. We identified 410 drivers of TFR, and classified 181 drivers into six main categories: Social, technological, environmental, economic, political, and health. The main factors that lower the fertility rate are education, aging, poverty, postponing marriage (social), exposure of women to mass media (technology), type of residence and living in urban areas, road accidents (environmental), income and economic situation (recession and inflation), unemployment, debt, women’s employment (economic), government policies (political), use of anti-fertility drugs (health). The main drivers that raise the fertility rate are having first child girls, women’s proximity to their workplace (social), use of assisted reproductive technology (technology), living in rural areas and natural and unnatural disasters (environmental), improvement in men’s labor market conditions, employment opportunities (economic), increased parental leave (political), high infant mortality (health). Conclusions: We identified a comprehensive list of effective drivers that have increased or decreased TFR in different parts of the world. It is necessary to review these drivers considering the specific conditions and social, cultural, economic, and political coordinates of each country. This review should inform policymakers' actions in this area.

GIS-Based Assessment of Best Route in Complex Traffic Environment: A Case of Kolkata Municipal Corporation

In an emergency like an urban fire, road accident, or critical condition of patients, the shortest possible route to reach the destination is one of the most sought-after issues in transportation studies. However, the physical distance of the shortest route becomes longer distance than the travel time distance due to traffic congestion in metropolitan cities like that of Kolkata. So, the selection of an alternative route is essential to organize the journey logically. A well-structured road network provides multiple options to road users for selection of the best route from the shortest and alternative route according to their demand. So, the present study aims to identify the structural characteristics of road networks by graph theory and the selection of the best route using the GIS technique. It has been found that the road network of Kolkata is characterized by complex structure and grid configuration of the network. Selection of the best route also ranges from the shortest route to alternative route due to spatial variation of traffic speed on different roads in peak hours. The current study identifies the optimal route in terms of travel time to reach the required destinations during peak hour duration in the city of Kolkata.

Mitigation of urban road collapses based on machine learning via integrating susceptibility assessment and geophysical detection validation

Road collapse is a frequent and damaging disaster in cities. The complexity and uncertainty inherent in urban environments pose significant challenges to mitigating road collapses. This paper presents a novel framework integrating machine learning-based susceptibility assessment and geophysical detection validation for urban road collapse risk reduction. Three oversampling techniques, random oversampling, synthetic minority oversampling technique for nominal and continuous features (SMOTENC), and adaptive synthetic sampling (ADASYN), are first utilized to implement data augmentation on urban road collapse accident samples. Subsequently, three machine learning models, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), are developed to evaluate road collapse susceptibility by extracting collapse-inducing patterns from historical accident data. Particularly, on-site geophysical hazard detection is conducted to validate the assessment results. The results demonstrate that XGBoost with SMOTENC achieves satisfactory performance in identifying road collapses with accuracy (0.9608) and AUC (0.9796). The spatial distribution of road collapse susceptibility in Shanghai central area follows a high-moderate-low pattern from northwest to southeast. The geophysical detection reveals a correlation between higher road collapse susceptibility and increased severity of underground diseases, validating the generalization capacity of XGBoost in actual operational environments. Additionally, the structural problems of underground pipelines are identified as the most influential factors for urban road collapse. This research offers valuable insights for urban road collapse mitigation and resilience improvement of transportation infrastructure.

Characteristics of malignant urban gas accidents in China from 2013 to 2022

Malignant urban gas accidents, accounting for only approximately 1 % of the total gas accidents in China, are responsible for over 50 % of fatalities, thus becoming a major cause of public concern regarding gas safety. A comprehensive understanding of the characteristics of historical accidents is an effective way to prevent and reduce future accidents. In this regard, the study meticulously collects and analyzes all malignant gas accidents that occurred in China from 2013 to 2022. This approach can effectively clarify the focus of gas accident prevention, and also facilitate the implementation of more targeted preventive measures. The study provides diverse perspectives and comprehensive statistics on accidents using 10 variables in 6 dimensions: time (year, month, day, hour), location (province, place), gas source, type, cause, and level. In addition to common quantitative statistics, proportional analyses and visual displays, methods such as contingency tables, t-tests, chi-square tests, and cluster analyses were also used to provide more in-depth analyses and identify more potential patterns. The findings elucidate that: (1) Over the past decade, the yearly distribution of malignant gas accidents in China has been relatively stable; but, individual, particularly major accidents, have significantly influenced the overall severity; (2) June and July are the peak months for such accidents; (3) The most severe accidents occur during 6:00–6:59 a.m., 11:00–11:59 a.m., and 11:00–11:59 p.m.; (4) Central and eastern provinces are the epicenters of malignant gas accidents; (5) Residential accommodations, and restaurants and shops are frequently affected; (6) Liquefied petroleum gas, despite comprising merely 8.7 % of the aggregate supply, is implicated in over half of the malignant occurrences and associated fatalities; (7) Explosions account for approximately 78.49 % of these accidents; (8) Among the accident causes, violations of rules by workers stands as the foremost contributor; (9) Malignant gas accidents are generally larger accident. Moreover, this manuscript delves into the underlying reasons behind each noteworthy statistical trend and analyzes the critical issues facing the gas industry. This investigation not only bridges the gap in the statistical profiling of malignant gas accidents within China but also furnishes invaluable insights that may guide preventive measures against urban gas-related disasters. Furthermore, the methodological approaches and variable selections explored in this study lay a robust foundation for future endeavors in the realm of malignant gas accident management research and practice.

Analyzing the effects of streetscape and land use on urban accidents and predicting future accidents by using machine learning algorithms (case study: Mashhad)

In general, land use and layout of streets can have a significant impact on the behavior of drivers and pedestrians. In particular, streetscape has often been overlooked that recognizing the role of streetscape on street accident in urban areas is important. The aim of this research is to investigate the influence of streetscape and land use on urban accidents that occurred in Mashhad between the years 2017 and 2021. To achieve this objective, the study focused on analyzing accidents in three different urban zones. It also considered the land use types adjacent to both closed and open streets, including residential, commercial, and mixed land uses. The research employed various surveys to gather the necessary data and insights related to the targeted areas. Statistics on accident in three zones show that among the mentioned land uses, commercial areas have experienced the highest number of accidents, with their share being approximately three times that of accidents in residential areas. Additionally, 75 % of all accidents took place in areas with open streetscape, whereas accidents in areas with enclosed view accounted for one third of the number of accidents in open streetscape areas. In this research, analysis and modeling were conducted using machine learning algorithms implemented in the Python programming language. Several models were employed, and the best models were selected based on their performance and accuracy, which include Random Forest Regression (RFR), Multilayer Neural Network Perceptron Regression (MLP) and Extreme Boost Gradient Regression (XGBoost). The accuracy of the machine learning models which successfully predicted future outcomes was as follows: Random Forest Regression (RFR) achieved 85 % accuracy, Extreme Boost Gradient Regression (XGBoost) achieved 81 % accuracy, and finally, Neural Network Multilayer Perceptron Regression (MLP) achieved 75 % accuracy.

Fusion of satellite and street view data for urban traffic accident hotspot identification

As the number of vehicles and the volume of traffic swell in urban centers, cities have experienced a concomitant increase in traffic accidents. Proactively identifying accident-prone hotspots in urban environments holds the promise of preventing traffic mishaps, thereby curtailing the incidence of accidents and reducing property damage. This research introduces the Two-Branch Contextual Feature-Guided Converged Network (TCFGC-Net) utilizing multimodal satellite and street view data. Designed to extract global structural features from satellite imagery and dynamic continuous features from street view imagery, the model aims to improve the accuracy of detecting urban accident hotspots. For the satellite imagery branch, we propose the Contextual Feature Coupled Convolutional Neural Network (Trans-CFCCNN) designed to extract global spatial features and discern feature correlations across adjacent regions. For the street view imagery branch, we develop the Sequential Feature Recurrent Attention Network (SFRAN) to assimilate and integrate dynamic scene features captured from successive street view images. We designed the Multi-Branch Feature Adaptive Fusion Structure (MBFAF) to aggregate different branch features for accurate identification of accident hotspots. Experimental results show that the model performs well, with an overall accuracy of 93.7 %. Ablation studies confirm that relative to standalone street view and satellite branch analyses, implementing multimodal fusion enhances the model's accuracy by 12.05 % and 17.86 %, respectively. The innovative fusion structure proposed herein garners a 4.22 % increase in model accuracy, outpacing conventional feature concatenation techniques. Furthermore, the model outperforms existing deep learning models in terms of overall efficacy. Additionally, to showcase the efficacy of the proposed model structure, we utilize Class Activation Maps (CAM) to provide visual interpretability for the model. These results suggest that the dual-branch fusion model effectively decreases false alarm occurrences and directs the model's focus toward regions more pertinent to accident hotspots. Finally, the code and model used for identifying hotspots of urban traffic accidents in this study are available for access: https://github.com/gwt-ZJU/TCFGC-Net.

What Leads to Effective Emergency Management? A Configurational Analysis of Empirical Cases of Local Chinese Governments

This study investigates the foundational elements that contribute to effective emergency management in urban settings, with a particular focus on experiences from Chinese municipalities. Drawing on resource dependence theory and attention allocation theory, this research develops an analytical framework encompassing four pivotal factors: environmental resources, information sharing, social participation, and leadership attention allocation. Utilizing Qualitative Comparative Analysis (QCA), we examine these factors across sixteen critical incidents of urban accidents and disaster responses in China. Our findings reveal that a high degree of leadership attention allocation is an essential prerequisite for municipal governments to exhibit robust emergency management capabilities during crises. Furthermore, two primary pathways affecting emergency management capacity were identified: the “resource–leadership attention type” and the “social participation–resource coordination type”. These findings contribute to a nuanced understanding of the complexities of emergency management and enlighten the local governments to take some effective measures to enhance emergency management capacities to mitigate disaster losses.

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Examining the impacts of accident characteristics and differentiated built environment factors on accident severity at inherent accident hotspots within cities can help managers to adjust traffic control measures through urban planning and design, thereby reducing accident casualties. In this study, time series clustering was used to identify traffic accident hotspots in Changsha City. Based on the hotspot identification results, Kruskal–Wallis tests were used to select differentiated built environment factors among different accident areas within the city. A severity analysis model for road crashes in Changsha’s hotspots, taking into account the built environment, was constructed using a Light gradient boosting machine (LightGBM). In addition, Shapley additive explanations (SHAP) were used to reveal the influences of accident characteristics and built environment factors on accident severity. The results showed that different accident characteristics and built environment factors affect accident severity in different urban accident areas. Factors such as type of accident, visibility, period of time, land use mixing degree, population density, density of commercial places, and density of industrial places showed varying degrees of importance in influencing accident severity, while the overall impact trends remained consistent. On the other hand, transportation accessibility, road network density, landform, and accident location showed significant differences in their impacts on accident severity between different accident areas within the city.

Ideology of Urban Road Transport Chaos and Accident Risk Management for Sustainable Transport Systems

Transport systems are complex systems present in modern cities. The sustainability of all other urban systems depends on the sustainable functioning of urban transport. Various processes occur within transport systems. Road traffic is one of them. At the same time, road traffic is a rather complex process to manage, which is explained by the influence of many different internal and external environmental factors. The unpredictable and chaotic behavior of each vehicle in a traffic flow complicates predicting the transport situation and traffic management. This problem gave rise to several unsolved problems, including traffic congestion and road accident rates. The solution to these problems is connected with sustainably managing transport systems in terms of road traffic. However, numerous regularities between elements within the system should be understood in order to implement the management process. Unfortunately, the results of many previous studies often reflect only partial regularities and have limited functionality. Therefore, a new approach to urban traffic management is needed. As opposed to the existing solutions, the authors of this paper propose to implement management based on the regularities of changes in the chaos of the transport system. In this regard, the purpose of this research is to establish the relationship between road traffic chaos and road accident rates. The general methodological basis of this research is the system approach and its methods: analysis and synthesis. The theoretical studies were mostly based on the theories of chaos, dynamic systems, and traffic flows. The experimental studies were based on the theories of experimental design, probability, and mathematical statistics. To achieve this goal, a profound analysis covered studies on the sustainability of transport and dynamic systems, sociodynamics, and traffic. The authors proposed considering the relative entropy of lane occupancy at signal-controlled intersections as a measure for assessing traffic flow chaos and sustainability. Notably, as the main conclusions, the authors established regularities for the influence of entropy on the kinetic energy of traffic flows and injury risk. It also makes sense to emphasize that the initial data for the experiment were collected via real-time processing of video images using neural network technologies. These technologies will further allow for the implementation of traffic management and real-time forecasting of various events. Ultimately, the authors identified changes in injury risk depending on the level of road chaos. According to the authors, the obtained results can be used to improve the sustainability of urban transport systems. The research identified changes in injury risk depending on the level of road chaos, which could have significant implications for urban traffic management strategies.

Methodology, application and platform for urban security planning and management

With the acceleration of economic development and urban construction, urban security accidents have occurred around the world with alarming frequency, causing serious casualties and economic losses. Urban security planning and management as emerging areas of research have drawn widespread attention. For city development plans, urban security planning and management have become one of major topics. This paper first outlines the principles of urban security planning and management, combined with the construction of a digital and intelligent platform for urban emergency management. This research then analyzes the core technology and equipment support system of urban security management and its practical application. It also presents a new model based on urban security planning and management, followed by examples of its application in some mega infrastructure development for security planning and design (for example, Singapore Changi Airport and Shanghai Hongqiao Airport Transportation Hub). Additionally, a blast protection concept of urban security planning and management is provided.

An integrated group decision-making framework for evaluating the urban fire emergency management capability based on PDHLWPHM and PDHLWPGHM operator

Urban fire accident is a common dangerous accident in urban sudden accidents, which threatens the safety of people’s lives and property. For this reason, in recent years, all cities have incorporated the prevention and emergency management of urban fire accidents into their urban development planning, and actively improved their fire accident emergency management capabilities. However, how to evaluate the urban fire accident emergency management capacity of each city to ensure that people’s lives and property are protected to the greatest extent is an urgent problem to be considered and solved. Therefore, this paper defines a class of probabilistic double hierarchy linguistic Heronian mean (PDHLHM) operator, probabilistic double hierarchy linguistic Power Heronian mean (PDHLPHM) operators, and their dual operators that can reflect the relationship between two attributes during aggregation. Taking urban fire accident risk monitoring and early warning capability, fire infrastructure and communication system, fire-fighting and rescue capability, recovery and reconstruction capability as evaluation attributes, the probabilistic double hierarchy linguistic weight Power Heronian mean (PDHLWPHM) operator model and the probabilistic double hierarchy linguistic weight Power geometric Heronian mean (PDHLWPGHM) operator model are constructed for group decision-making. In addition, the idempotence, boundedness and monotonicity of these operators are studied, and the sensitivity of the parameters involved in the operator model is analyzed. Finally, the new model proposed in this paper is compared with the existing model to verify its scientificity.

Role of Management of Urban Disasters and Accidents In the Sense of Security and Welfare of Citizens

Role of Management of Urban Disasters and Accidents In the Sense of Security and Welfare of Citizens

A comprehensive analysis method of urban rail transit operation accidents and safety management strategies based on text big data

Although the occurrence of urban rail transit operation accidents threatens the safety of people's lives and property, there is little research on how to systematically mine the knowledge behind accident data in China, and there is also lack of methods to support the safety management strategy of urban rail transit operation. To address these shortcomings, this study proposes a comprehensive analysis method that combines qualitative and quantitative analysis of urban rail operation accident factors and safety management strategies based on accident data, which can provide reference for systematic analysis of such accidents in the future and will help urban rail safety researchers and decision makers to take effective safety management measures. The new contributions of the method are as follows: (1) a semantic network framework for accidents and a conceptual model of accident risk control chain are proposed; (2) a method for acquiring and constructing key contents based on semantic analysis is elaborated, and indicators for evaluating the influence degree of key item and the influence possibility of entries are proposed; (3) a management matrix is established to serve safety decision making based on indicators for the degree of attention that key item of accidents should receive. In order to explain the application of the comprehensive analysis method, 910 urban rail transit operation safety accident cases are analyzed in detail, and suggestions for the actual operation management of urban rail transit are proposed in the light of the actual situation of urban rail transit operation in China.

A Case Study on Detection of Cavity in Urban Roads Using 3D Ground Penetrating Radar

In recent years, urban roads collapse accidents often occur, which seriously threatens the safety of people’s lives and property and the order of urban operation, and has a huge social impact. Therefore, it is very necessary to carry out road cavity detection and eliminate the hidden dangers of road operation safety. Three dimensional ground penetrating radar (GPR) technology is a new technology developed in recent years. At present, it has been widely used in road detection. This paper briefly introduces the process of urban roads detection using 3D GPR, and introduces an example of cavity detection. The results show that the effect of 3D GPR in road cavity detection is remarkable, which is worth popularizing.

Risk analysis of waterlogging in a big city based on a bow-tie Bayesian network model, using the megacity of Wuhan as an example

At present, the global urbanization process is accelerating, with the climate changing constantly and extreme weather increasing. In this background, urban flood disasters caused by rainstorms frequently occur in China. Therefore, a disaster risk analysis model based on a bow-tie Bayesian network was established to analyze the risk of waterlogging disasters. First, the waterlogging accident was analyzed, the intermediate and basic events that caused the accident were identified, and the accident tree was drawn. According to the intuitive nature of the fault tree, it was transformed into a Bayesian network, and in the meantime, a posteriori probability analysis of nodes was performed to further obtain the critical importance of basic events. By analyzing the importance of the basic events of waterlogging disasters, the key basic events that lead to disasters were proposed. Finally, the bow-tie model was used to analyze the importance of the hazards, and a strategy for the prevention and control of accidents was proposed. The results show that the major accident node of urban waterlogging accidents is the unsafe state of the urban environment, and the management and control of the urban environment should be strengthened to improve the prevention and control of urban waterlogging, e.g., pre-disaster prevention.

The Evaluation and Obstacle Analysis of Urban Safety Resilience Based on Multi-Factor Perspective in Beijing

Urban resilience assessment is crucial to guarantee the safe and stable operation of our cities. Looking at the whole process of emergencies from the occurrence, development, causing disaster losses to taking emergency response measures, we combine the danger of urban accident and disaster, the capacity of disaster-bearing carriers, and the emergency management capacity as the influencing factors. Taking Beijing as an example, considering the completeness of the relevant indicators and the difficulty of obtaining data, TOPSIS is used to construct a multi-factor comprehensive assessment model of urban safety resilience; At the same time, the important factors constraining the improvement of safety resilience are further analyzed by using the obstacle degree model. The results show that: among the 16 districts in Beijing, Chaoyang and Haidian districts have the highest level of safety resilience; the level of the degree of disaster tolerance among the multi-factors has the greatest impact on the level of urban safety resilience in Beijing; and the multifactorial comprehensive assessment model of urban resilience is able to reflect the urban resilience distribution, discover the weaknesses that exist in the development of urban safety and provide decision-making aids for the relevant city managers to carry out urban resilience construction.

Dynamic risk investigation of urban natural gas pipeline accidents using Stochastic Petri net approach

The safety of urban gas pipelines is challenged by a series of adverse factors, and the unexpected accidents may pose a catastrophic threat to humans, the environment and assets. The existing studies mainly focused on causation investigation of natural gas pipeline accident, the dynamic evolution process of urban natural gas pipeline accident is still challenging task for accident prevention. To find out the unfavorable factors that cause accidents, this study presents a dynamic risk modeling of urban natural gas pipeline accidents using Stochastic Petri net (SPN). An SPN model of an accident evolution process is constructed based on the discrete events in an accident flowchart, and the critical places and transitions are evaluated through this model. Considering the slow development of the early events leading to accidents, the delay time of the transitions at this stage cannot be determined, Bayesian theory is used to dynamically update SPN model. Critical accident nodes and their occurrence probabilities are estimated, which are used to support the efficient risk management strategies. The gas explosion accident in Shiyan, Hubei, as a representative case is investigated, and the results show that pipeline corrosion, ignition sources, inefficient information feedback and unreasonable solutions are critical accident causations. The probabilities of critical accident nodes increase over time, which means that these factors need to be managed efficiently to prevent the accidents. The application shows that the model can be used as a tool for gas companies and governments to investigate urban gas pipeline accidents.

A Narrative of Urban Underground Pipeline System Disasters in China in 2021: Spatial and Temporal Distribution, Causal Analysis, and Response Strategies

In recent years, there have been frequent disasters and accidents in the underground pipeline system of Chinese cities, posing a continuous threat to the safety of life and property of the public and the order of urban operations. This article uses statistical data on the spatiotemporal distribution of major underground pipeline disasters in various provinces of China in 2021. By using Pearson’s correlation coefficient and regression analysis, a causal analysis model for urban underground pipeline accidents is constructed, and the correlation between urban underground pipeline accident rate and economic and social indicators is analyzed. From the analysis, it has been found that the correlation coefficients of gross domestic product (GDP) per capita, the pipeline density in the built-up area, and the urban pipeline accident rate reach −0.4019 and −0.4275, respectively, showing a negative correlation. Further, the regression analysis results show that the underground pipeline accident rate shows a decreasing trend as the per capita GDP and the density of pipelines in the built-up areas increase. Among them, the results of the power function fitting model show that the GDP per capita accounts for 48.10% of the urban underground pipeline accidents, whereas the pipeline density of the built-up area accounts for 58.27% of these accidents. The construction of underground pipeline regulations is influential in reducing underground pipeline disaster accidents. In this study, the role and effectiveness of the factors, such as the construction of professional regulations in maintaining the safe operation of urban underground pipelines, has been discussed, and suggestions and methods to improve the problems that need to be solved, such as the construction of a municipal supporting emergency management system in the safe operation of underground pipelines in the future, have been proposed.

Building urban gas process safety management (UG-PSM) system: Based on root cause analysis with 160 urban gas accidents in China

Urban safety is significantly impacted by the complexity of urban gas accidents. Although China has put forward the goal of “zero fatalities in accidents” for urban gas, unfortunately, the root causes of the unsound safety culture and imperfect safety management system of urban gas enterprises remain unresolved. Therefore, statistical analysis and 24Model analysis of 160 urban gas accidents in mainland China were performed to investigate the proximate causes of the accidents and the current situation of urban gas safety. The CCPS's risk-based process safety elements were used to identify potential deficiencies in the current urban gas process safety management (UG-PSM). During the analysis, it was observed that insufficient implementation of concealed danger investigation and rectification accounted for 76%, which is the primary proximate cause of urban gas accidents. Stakeholder outreach is the most under-represented competency in urban gas safety management. Based on the results, a novel framework for the UG-PSM system was constructed, and the theory of urban gas safety management was further improved. Furthermore, we evaluated the matching degree between UG-PSM elements and the existing measures of the government, urban gas associations, third-party organizations, and urban gas enterprises. Finally, we analyzed the feasibility, challenges, and development directions of the UG-PSM system. This study establishes a foundation for researchers and practitioners in the future research and practice of urban gas process safety and provides theoretical guidance for preventing high-incidence accidents of urban gas in China.