Search Results (37)

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

Predicting pit deformation to prevent safety accidents is the primary objective of pit deformation forecasting. A reliable predictive model enhances the ability to accurately monitor future deformation trends in pits. To enhance the prediction of pit deformation and improve accuracy and precision, an Improved Crown Porcupine Optimization Algorithm (ICPO) based on a Bidirectional Time Convolution Network–Bidirectional Gated Recirculation Unit (BITCN-BIGRU) is developed. This model is utilized to forecast the future deformation trends of the pit. Utilizing site data from a metro station pit project in Chengdu, the accuracy of the predicted values from Historical Average (HA), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models is evaluated against the six models developed in this study, including the ICPO-BITCN-BIGRU model. Comparison of the test results indicates that the ICPO-BITCN-BIGRU prediction model exhibits superior predictive performance. The predicted values from the ICPO-BITCN-BIGRU model demonstrate R² values of 0.9768, 0.9238, and 0.9943, respectively, indicating strong concordance with the actual values. Consequently, the ICPO-BITCN-BIGRU prediction model developed in this study exhibits high prediction accuracy and robust stability, making it suitable for practical engineering applications. Full article

Recently, subsea tunnel construction has developed rapidly in China. The traffic volume of subsea metro tunnels is large. Once a safety accident occurs, economic losses and social impacts will be extremely serious. To eliminate accidents in operational subsea metro tunnel structures, a health risk prediction method is proposed based on a discrete Bayesian network. Detecting and monitoring data of the tunnel structures in operation were used to evaluate the health risk by employing the proposed method. This method establishes a Bayesian network model for the health risk prediction of the shield tunnel structure through the dependency relationship between the health risk of the operational tunnel structure and 13 risk factors in five aspects: the mechanical condition, material performance, integrity state, environmental state, and deformation state. By utilizing actual detection and monitoring data of various risk factors for the health risk of the operational subsea metro shield tunnel structure, this method reflects the actual state of the tunnel structure and improves the accuracy of health risk predictions. The validity of the proposed method is verified through expert knowledge and the subsea shield tunnel structure of the Dalian Subway Line 5. The results demonstrate that the health risk prediction outcomes effectively reflect the actual service state of the shield tunnel structure, thus providing decision support for the control of health risks in the subsea metro shield tunnel. Full article

Under the development trend of complexity and systematization of metro construction, there is an increasing number of risk factors potentially affecting construction safety, which has led to frequent accidents in metro construction projects, and the road to high-quality and sustainable development of metro construction is full of challenges. One of the essential reasons is that the propagation mechanism of safety risk factors in metro construction under hidden and delayed effects is not yet clear. This paper combines the theory of complex network and propagation dynamics and constructs a subway construction safety risk propagation model based on considering the hidden and delayed characteristics of construction safety risk propagation, which reveals the dynamic propagation law of subway construction safety risk and puts forward feasible coping strategies. The findings evince that the delay time T significantly affects the propagation behavior of risk and the achievement of the equilibrium state in the network. The transmissibility of the risk factor within the hidden state holds a pivotal sway over the entirety of risk propagation, and the latency in transmission significantly expedites the propagation of risk throughout the network. It is recommended that project managers monitor and warn safety state nodes and hidden state nodes to block the spread of risk in the network and control the delay time of risk in the network in time to reduce the probability of risk occurrence. This study significantly promotes the resilient management of safety risks in metro construction. Full article

This study aims to investigate the factors and consequences of gas deflagration accidents in metro shield tunnels based on on-site investigation and numerical analysis. We built a numerical model and detection process for an underground shield tunnel subjected to an internal explosion from an actual accident. The tunnel geometry under consideration is the same as that used for the metro line. Concerning the limitations of research on the failure and recovery mechanism of shield segmental linings under the action of internal explosion load, an explosion accident of a shield segmental lining under construction caused by the shield tunneling machine destroying natural gas pipelines was discussed, in which the structure failure characteristics during the explosion and the structure repair method after the explosion were investigated. An interval repair scheme was proposed, which provides experience for the treatment of similar engineering accidents. To investigate the gas explosion within the tunnel during the accident, the finite element software Ansys LS-DYNA with the arbitrary Lagrangian–Eulerian (ALE) technique was employed to simulate the explosion scenario. Dynamic analyses were carried out to reproduce the blast scenario. The stress distribution within the segmental lining as well as the lining’s deformation were calculated. The potential applications of the treatment and planning of comparable engineering mishaps were discussed in the study. Full article

Train distance estimation in a turnout area is an important task for the autonomous driving of urban railway transit, since this function can assist trains in sensing the positions of other trains within the turnout area and prevent potential collision accidents. However, because of large incident angles on object surfaces and far distances, Lidar or stereo vision cannot provide satisfactory precision for such scenarios. In this paper, we propose a method for train distance estimation in a turnout area based on monocular vision: firstly, the side windows of trains in turnout areas are detected by instance segmentation based on YOLOv8; secondly, the vertical directions, the upper edges and lower edges of side windows of the train are extracted by feature extraction; finally, the distance to the target train is calculated with an appropriated pinhole camera model. The proposed method is validated by practical data captured from Hong Kong Metro Tsuen Wan Line. A dataset of 2477 images is built to train the instance segmentation neural network, and the network is able to attain an MIoU of 92.43% and a MPA of 97.47% for segmentation. The accuracy of train distance estimation is then evaluated in four typical turnout area scenarios with ground truth data from on-board Lidar. The experiment results indicate that the proposed method achieves a mean RMSE of 0.9523 m for train distance estimation in four typical turnout area scenarios, which is sufficient for determining the occupancy of crossover in turnout areas. Full article

The government of China has planned numerous metro projects, and with more metros, undercrossing of bridges can hardly be avoided. Metro shield construction when undercrossing a bridge (MSCUB) frequently takes place in complicated natural and social contexts, which often makes the construction process more susceptible to safety accidents. Therefore, it is crucial to look into the safety risk during MSCUB. This paper identified the safety risk factors during MSCUB by using a literature review and expert group evaluation, proposed a novel safety risk assessment model by integrating confirmatory factor analysis (CFA) and fuzzy evidence reasoning (FER), and then selected a project case to test the validity of the suggested model. The study results show that (a) a safety risk factor list for MSCUB was identified, including four first-level safety risk factors and thirty-seven second-level safety risk factors; (b) the proposed safety risk assessment model can be used to measure the risk values of the overall safety risk of a worksite, the first-level safety risk factors, and the second-level safety risk factors during MSCUB; (c) environment-type safety risk factors and personnel-type safety risk factors have higher risk values during shield construction when undercrossing a bridge; (d) when compared with worker-type safety risk factors, manager-type safety risk factors are the higher risks. This study can enrich the theoretical knowledge of MSCUB safety risk assessment and provide references for safety managers for conducting scientific and effective safety management on a construction site when constructing metro shields undercrossing a bridge. Full article

In order to effectively reduce the probability of subway operation accidents and explore the key risk factors and multi-factor risk coupling mechanism during the subway operation period, this paper classifies the risk factors affecting subway operation safety into four categories of primary risk factors, personnel, equipment and facilities, environment and safety management, introduces the emergency management concept to identify 18 secondary risk factors, combines the improved fuzzy decision making test and evaluation laboratory (DEMATEL) and Explanatory Structure Model (ISM) to visualize the risk factor action relationship, construct a six-order hierarchical recursive structure model for subway operation accidents, explore the coupling relationship and effect between risk factors from the perspective of single factor, double factor and multiple factors, establish a coupling effect metric model based on Natural Killing Model (N-K), carry out coupling information interaction scenario combination and coupling effect quantification calculation, and finally integrate fuzzy DEMATEL-ISM-NK model to correct the centrality, determine the key risk factors in subway operation accidents from the perspective of macro and micro analysis, qualitative and quantitative research, and propose safety prevention and control strategies accordingly. The results show that six factors, such as emergency management and social environment, are key risk factors to be prevented in the metro operation system. Multi-factor risk coupling leads to a higher probability of subway operation accidents, and controlling multi-factor involvement in coupling is an effective means to reduce the occurrence of subway operation accidents. Full article

Rational use of urban underground space (UUS) and public transportation transfer underground can solve urban traffic problems. Accurate short-term prediction of passenger flow can ensure the efficient, safe, and comfortable operation of subway stations. However, complex and nonlinear interdependencies between time steps and time series complicate such predictions. This study considered temporal patterns across multiple time steps and selected relevant information on short-term passenger flow for prediction. A hybrid model based on the temporal pattern attention (TPA) mechanism and the long short-term memory (LSTM) network was developed (i.e., TPA-LSTM) for predicting the future number of passengers in subway stations. The TPA mechanism focuses on the hidden layer output values of different time steps in history and of the current time as well as correlates these output values to improve the accuracy of the model. The card swiping data from the Hangzhou Metro automatic fare collection system in China were used for verification and analysis. This model was compared with a convolutional neural network (CNN), LSTM, and CNN-LSTM. The results showed that the TPA-LSTM outperformed the other models with good applicability and accuracy. This study provides a theoretical basis for the pre-allocation of subway resources to avoid subway station crowding and stampede accidents. Full article

This research evaluated gender differences in the perception of safety in public transport in Santiago, Chile using quantitative and qualitative approaches. With data from the National Urban Citizen Security Survey 2019 (ENUSC), a gender comparison was made regarding the perception of safety in four scenarios: inside buses, inside the metro, at bus stops, and waiting for buses at night. Four ordinal logistic regression models were estimated to analyze how sociodemographic factors and variables associated with the perception of crime influence rider perceptions of safety in public transport. To complement the results, four focus groups were developed to obtain a deep understanding of the participants’ experiences with safety in the Santiago public transport system. We concluded that there is a high perception of insecurity in public transport for both men and women. In general, perceived insecurity inside buses, inside the metro, and waiting for public transport at night is greater among women, older people, and national citizens. Other influencing variables are the perception of insecurity regarding crime in general, the fear of being a victim of a crime, or negative situations that occur in the neighborhood, such as the presence of robberies, alcohol, and drug consumption. We proposed new variables such as fear of harassment, traffic accidents, discrimination, contagious diseases, and street protests among others. To carry out a precise public policy on this matter, a permanent scan on security issues in public transport should be developed, considering a complete set of variables. This result can be applied in Chile and all Latin American countries. Full article

Deep foundation pit (DFP) projects have been a high incidence area of safety accidents because of their own high danger and complexity. Therefore, it is necessary to study the resilience of their construction safety system. This paper systematically identifies the key factors affecting the resilience of deep foundation pit construction based on the analysis of the composition of the deep foundation pit construction safety system (DFPCTSS), the synergistic relationship of its subsystems in the face of the interference and impact of internal and external disaster-causing factors, and the causal mechanism of typical accidents in DFP accidents and the emergent process of system resilience. A resilience evaluation indicator system based on four capacity dimensions of prevention absorption, resistance, recovery, and learning adaptation was constructed by using the fuzzy Delphi method, which is characterized by the resilience emergence process. Then the correlation and weight of evaluation indexes were analyzed based on the DEMATEL–ANP method, the boundary cloud parameters of the resilience evaluation grade were set according to the normal extension cloud model, and the membership degree of the resilience evaluation level was calculated to complete the evaluation of the resilience level. Finally, taking a DFP project of a metro station as an example, the above model was used to evaluate the resilience level of its construction safety system, and suggestions for resilience enhancement were put forward. The results show that the evaluation results are consistent with the actual situation of the project, and the evaluation model is conducive to providing a systematic analysis method and improvement countermeasures for deep foundation pit construction safety management from the perspective of resilience. Full article

Gathering experience and organizing knowledge from a large number of engineering construction projects is conducive to more effective and efficient safety risk management in construction projects. Metro construction practitioners often find it difficult to determine what professional knowledge is needed to establish better management. By constructing the knowledge structure of safety risk management, which is composed of domain knowledge entities (DKEs) and their hierarchical relations, practitioners can systematically master the knowledge of safety management, enhance safety management levels, and reduce the occurrence of accidents. Traditionally, domain knowledge structure was determined by experts, the mistakes occur due to the limitations of individual knowledge, and high time costs are unavoidable due to the massive amount of data. Therefore, in this study, we used a rule-based Chinese-language natural language processing (C-NLP) method to automatically extract the hierarchical relations between DKEs from a large dataset of unstructured text documents; we aimed to clarify the affiliation relationship and parallel relationship between DKEs. First, 68,817 sources of literature written in Chinese were collected. Next, the specific syntactic structures of relations of the DKEs were analyzed. Hierarchical extraction rules, including 16 hyponymic indicators and 8 appositive indicators, were revealed based on the linguistic characteristics. Then, the relations were extracted from test dataset. The precision and recall values were used to verify the model. Finally, the hierarchical relations of all the DKEs were extracted, and the knowledge structure was formed. The proposed method of hierarchical relation extraction contributes to the quick automatic construction of knowledge structures and minimizes expert bias. The knowledge structures can be used to guide safety training and can assist practitioners in safety risk management. Full article

Safety problems in metro construction occur frequently, causing substantial economic losses and even resulting in injuries and fatalities. Studies have shown that human errors, which are usually caused by complex reasons, are an important cause of safety related accidents. However, little research has analyzed the causes of accidents from the perspective of human errors. To explore the factors influencing human errors, the factors were systematically sorted out and studied based on theoretical analysis. Firstly, the theoretical hypothesis and model were formulated through a literature review. Secondly, the scale was developed for mental factors, physical factors, technical factors, environmental factors, organizational factors, cultural factors, and human errors. Thirdly, the research data were obtained by distributing questionnaires, and the validity and reliability tests were conducted using the data and the structural equation model was tested and run. Finally, the theoretical hypotheses were tested using the structural equation models and came up with the paths of the six factors of human errors. The results of the study showed that mental factors, physiological factors, and technological factors are found to be the direct influencing factors of human errors. However, environmental and cultural factors are the indirect influencing factors. The influencing paths are environment-mental-human errors, environment-physiological-human errors, culture-physiological-human errors, and culture-technology-human errors. Organizational factors can affect human errors directly or indirectly through cultural factors. These findings could provide practical implications for reducing the safety related accidents caused by human errors during metro construction. Full article

Emergency management research is used to deal with the increasing number of extreme weather threats in urban areas. This paper uses causal analysis based on systems theory (CAST) to review the subway water ingress accident and the government’s emergency management actions in Zhengzhou, Henan Province, during the heavy rainstorm disaster on 20 July 2021. The aims of this article are to establish safety control structures at both the enterprise level and the government level, and to systematically analyze the problems in emergency management in Zhengzhou City. Our analysis found that the construction of disaster prevention facilities restricted emergency management. Therefore, we suggest that enterprises and governments not only pay attention to emergency management, but also to the construction of disaster prevention facilities. This article also points out that the system of chief executive responsibility that is implemented in China is becoming a double-edged sword in emergency management. Our study makes recommendations for enhancing the capacities of emergency management, points out the shortcomings of the existing emergency management structure, and provides knowledge gained for future emergency management research. Full article

In this study, we investigated the flooding accident that occurred on Metro Line 5 in the capital city of Zhengzhou, Henan Province, China. On 20 July 2021, owing to an extreme rainstorm, serious inundation occurred in the Wulongkou parking lot of Zhengzhou Metro Line 5 and its surrounding area. Flooding forced a train to stop during operation, resulting in 14 deaths. Based on our preliminary investigation and analysis of this accident, we designed three main control measures to reduce the occurrence of similar accidents and mitigate the impact of similar accidents in the future, given the increasing number of extreme storm weather events in recent years: (1) to conduct subway flood risk assessments and to establish an early warning system, involving real-time monitoring of meteorological information during subway operation and construction; (2) to improve subway flood control emergency plans and to establish a response mechanism for subway flooding; and (3) to strengthen safety awareness training to ensure the orderly evacuation of people after accidents. Full article