An analysis of escalator-related injuries in metro stations in China, 2013-2015

Risk Assessment of Passenger Behaviors That Influence Accident Type and Severity in Metro Operation

Background

The unsafe behavior of passengers frequently causes metro operation accidents. This research aims to establish a model for evaluating the risk of unsafe behavior among subway passengers and for assessing the severity of different types of accidents caused by passenger unsafe behavior.

Methods

A risk assessment model that combines the Interaction Matrix (IM) model with a Monte Carlo algorithm was established to quantitatively test the risk of unsafe behavior among passengers. Based on the initial data of 234 cases, the behavioral risks in accidents were simulated, and the resulting risks follow a normal distribution. After analyzing the differences in behavioral risk distribution characteristics, the targeted risk mitigation countermeasures were obtained.

Results

Results showed that there are 12 kinds of unsafe behaviors related to 4 metro operation accident types. Among them, crowded stampede caused by four kinds of passengers' unsafe behavior has the highest risk mean (μ) of 5.14, followed by escalator injury (4.72), pinched by a shielding barrier (4.42) and fall injury (4.14).

Conclusion

The severity of different types of accidents caused by different unsafe behaviors of passengers was obtained, which can provide a basis for targeted risk mitigation strategies and measures.

Come together: A unified description of the escalator capacity

We investigate a variety of aspects related to the simulation of passenger dynamics on escalators, mainly focusing on the discrepancy between the 'theoretical' and the 'practical' capacity that is observed for these facilities. The structure of the paper is twofold. In the first part, we introduce a space-continuous model to describe the transition of agents from walking on the plain to standing on the escalator. In the second part, we use numerical findings from simulations to study important measures like minimum distances between the standing agents and average occupancies of the escalator steps. One of the most important results obtained in this paper is a generalized analytical formula that describes the escalator capacity. We show that, apart from the conveyor speed, the capacity essentially depends on the time gap between entering passengers which we interpret as human reaction time. Comparing simulation results with corresponding empirical data from field studies and experiments, we deduce a minimum human reaction time in the range of 0.15s-0.30s which is in perfect agreement with results from social psychology. With these findings, it is now possible to determine accurately the relationship between the capacity and the speed of an escalator, allowing a science-based performance evaluation of buildings with escalators.

Analysis of Factors Influencing Public Behavior Decision Making: Under Mass Incidents

Most mass incidents are created by economic or social concerns brought on by fast socioeconomic change and poor local government. The number of mass occurrences in China has significantly increased in recent years, putting the country’s steady growth and public behavior decision-making in harm. We examine the factors that influence public behavior decision-making in the following significant factors, contributing to the development of effective prevention and response strategies. The structural equation (SEM) approach is used to analyze the main determinants influencing public behavioral decisions in the aftermath of mass incidents using surveys of a large population. The finding shows that media plays a mediating role in the relationship between mass occurrences and influencing factors impacting public emotion. The direct and indirect effects of public behavior decision-making and its role increasingly social changes as things happen, government credibility, media plays mediating role in public emotional factors. All directly impact public behavior decision-making, while emotional factors have an indirect impact via media intermediaries. The escalation of public behavior decisions is seen as a result of structural transmission and the increase of dynamic as well as other factors.

Grid-Based Employee Safety Behavior Risk Assessment of the Train Operation Department

In the train operation department, the most important and dynamic factor is that the department employees are involved in all areas. Realizing the dynamic control of “key person, key event, and key period” to fundamentally curb employee inertia violation is a significant issue that needs to be solved on the railway site. The traditional “probability–severity” two-dimensional risk assessment model is carried out from the perspective of the system, ignoring the spatiotemporal risk characteristics of the individual, and a large amount of hazard factor data generated in the operation process is not applied in the risk assessment process. As a result, safety behavior risk practice lacks pertinence, accuracy, and individuation. This study proposes a safety behavior risk assessment model based on the grid management and hazard factor assignment function to improve the traditional two-dimensional risk matrix. By introducing spatial location variables, the method accurately locates and classifies the site staff and organizes the disorder and lack of associated risk data with regard to time and space. With a focus on the hazard factor, the induced intensity is proposed for the first time and considered as the input of probability calculation to innovate the traditional “probability–severity” risk matrix. Finally, the methodology is applied to the risk event assessment of “the assistant watchman doesn’t appear as required” scenario in the Huangyangcheng station of Shenshuo Railway, and the evaluation results realize the personalized evaluation of the risk event in different cell grids.

Gray relational analysis based assessment of escalator accident risk in subway stations

In order to better achieve active defense in the escalator risk management, this study based on the vulnerability theory, task driven theory, management error theory, proposed a Gray Relational Analysis (GRA) based fuzzy assessment of escalator accident risk approach. The risk assessment index system of subway station escalator accident was constructed based on the commonness and essence of management defects; the weight of risk index was calculated scientifically and reasonably by using Analytic Hierarchy Process (AHP); escalator accident risk was evaluated by the combination of GRA and Fuzzy approach. The results show that escalator equipment, environment, safety knowledge of riders are all in good condition in the station. However, ‘Maintenance’ of escalator in the Beijing subway station is in an extremely high risk level. The contributions of this studies are: (1) general risk elements analysis model for escalator accidents which enable to compose any risk factor possible to induce escalator accident in subway station; (2) GRA based risk assessment approach can avoid the problem when expend the range to left and right. It can also judge whether the continuous improvement effect of the object is significant by the difference degree of each risk level before and after.

Contributing Factors Affecting the Severity of Metro Escalator Injuries in the Guangzhou Metro, China

Urban rail transit has become one of the indispensable modes of public transportation in large cities. Escalators are ubiquitous in metro stations, as passengers typically use escalators when entering or leaving a metro station. Thus, escalators have become an accident-prone location. To develop suitable prevention strategies, it is necessary to understand the risk factors that affect the severity of escalator accidents. This study analyzed 967 escalator passenger accidents that occurred in the Guangzhou Metro from 2013 to 2015. The Haddon matrix was used to evaluate the interaction of humans, escalators, and environmental factors before, during, and after accidents. Then, the contributing factors associated with the severity levels were determined based on chi-square tests. Passengers aged 66 years and older are more vulnerable to serious injuries (p < 0.001), and previous health conditions are significantly related to the severity of the passenger’s injuries (p = 0.002). The weather conditions (rainy days) are also significantly related to the severity of escalator accident injuries (p = 0.039), and injured people with head injuries are at greater risk of being severely injured (p < 0.001). The analysis results of these risk factors can provide theoretical support for the metro operators to develop reasonable and effective preventive measures to reduce the escalator risk.

The Next Failure Time Prediction of Escalators via Deep Neural Network with Dynamic Time Warping Preprocessing

The escalator is one of the most popular travel methods in public places, and the safe working of the escalator is significant. Accurately predicting the escalator failure time can provide scientific guidance for maintenance to avoid accidents. However, failure data have features of short length, non-uniform sampling, and random interference, which makes the data modeling difficult. Therefore, a strategy that combines data quality enhancement with deep neural networks is proposed for escalator failure time prediction in this paper. First, a comprehensive selection indicator (CSI) that can describe the stationarity and complexity of time series is established to select inherently excellent failure sequences. According to the CSI, failure sequences with high stationarity and low complexity are selected as the referenced sequences to enhance the quality of other failure sequences by using dynamic time warping preprocessing. Secondly, a deep neural network combining the advantages of a convolutional neural network and long short-term memory is built to train and predict quality-enhanced failure sequences. Finally, the failure-recall record of six escalators used for 6 years is analyzed by using the proposed method as a case study, and the results show that the proposed method can reduce the average prediction error of failure time to less than one month.

An Ontological Metro Accident Case Retrieval Using CBR and NLP

Metro accidents are apt to cause serious consequences, such as casualties or heavy economic loss. Once accidents occur, quick and accurate decision-making is essential to prevent emergent accidents from getting worse, which remains a challenge due to the lack of efficient knowledge representation and retrieval. In this research, an ontological method that integrates case-based reasoning (CBR) and natural language processing (NLP) techniques was proposed for metro accident case retrieval. An ontological model was developed to formalize the representation of metro accident knowledge, and then, the CBR aimed to retrieve similar past cases for supporting decision-making after the accident cases were annotated by the NLP technique. Rule-based reasoning (RBR), as a complementary of CBR, was used to decide the appropriate measures based on those that are recorded in regulations, such as emergency plans. A total of 120 metro accident cases were extracted from the safety monthly reports during metro operations and then built into the case library. The proposed method was tested in MyCBR and evaluated by expert reviews, which had an average precision of 91%.

Critical Hazards Identification and Prevention of Cascading Escalator Accidents at Metro Rail Transit Stations

Escalator accidents not only happen frequently but also have cascading effects. The purpose of this study is to block the formation of cascading accident networks by identifying and preventing critical hazards. A modified five-step task-driven method (FTDM) is proposed to break down passenger-related cascading escalator accidents. Three complex network parameters in complex network theory are utilized to identify critical and non-critical Risk Passenger Behavior (RPB) hazards and Other Hazards related with Risk Passenger Behavior (OH-RPB) in accident chains. A total of 327 accidents that occurred in the Beijing metro rail transit (MRT) stations were used for case studies. The results are consistent in critical and non-critical RPB and OH-RPB and prove that through combination of FTDM accident investigation model and complex network analysis method, critical and non-critical RPB and OH-RPB in a complicated cascading hazards network can be identified. Prevention of critical RPB can block the formation of cascading accident networks. The method not only can be used by safety manager to make the corresponding preventive measures according to the results in daily management but also the findings can guide the allocation of limited preventive resources to critical hazards rather than non-critical hazards. Moreover, the defects of management plan and product design can be re-examined according to the research results.

Analysis Factors That Influence Escalator-Related Injuries in Metro Stations Based on Bayesian Networks: A Case Study in China

Escalator-related injuries have become an important issue in daily metro operation. To reduce the probability and severity of escalator-related injuries, this study conducted a probability and severity analysis of escalator-related injuries by using a Bayesian network to identify the risk factors that affect the escalator safety in metro stations. The Bayesian network structure was constructed based on expert knowledge and Dempster–Shafer evidence theory, and further modified based on conditional-independence test. Then, 950 escalator-related injuries were used to estimate the posterior probabilities of the Bayesian network with expectation–maximization (EM) algorithm. The results of probability analysis indicate that the most influential factor in four passenger behaviors is failing to stand firm (p = 0.48), followed by carrying out other tasks (p = 0.32), not holding the handrail (p = 0.23), and another passenger’s movement (p = 0.20). Women (p = 0.64) and elderly people (aged 66 years and above, p = 0.48) are more likely to be involved in escalator-related injuries. Riding an escalator with company (p = 0.63) has a relatively high likelihood of resulting in escalator-related injuries. The results from the severity analysis show that head and neck injuries seem to be more serious and are more likely to require an ambulance for treatment. Passengers who suffer from entrapment injury tend to claim for compensation. Severe injuries, as expected, significantly increase the probability of a claim for compensation. These findings could provide valuable references for metro operation corporations to understand the characteristics of escalator-related injuries and develop effective injury prevention measures.

Hazard Analysis for Escalator Emergency Braking System via System Safety Analysis Method Based on STAMP

Due to the complex mechanical structure and control process of escalator emergency braking systems (EEBS), traditional hazard analysis based on the event chain model have limitations in exploring component interaction failure in such a complex social-technical system. Therefore, a hazard analysis framework is proposed in this paper for hazard analysis of complex electromechanical systems based on system-theoretic accident model and process (STAMP). Firstly, basic principles of STAMP are introduced and comparison with other hazard analysis methods is conducted, then the safety analysis framework is proposed. Secondly, a study case is performed to identify unsafe control actions of EEBS from control structures, and a specific control diagram is organized to recognize potential example casual scenarios. Next, comparison between fault tree analysis and STAMP for escalator’s overturned accident shows that hazards related to component damaged can be identified by both, while hazards that focus on components interaction can only be identified by STAMP. Besides, single control way and tandem operation process are found to be the obvious causal factors of accidents. Finally, some improvement measures like decibel detection or vibration monitoring of key components are suggested to help the current broken chain detection to trigger the anti-reversal device for a better safe EEBS.

Factors Influencing Escalator-Related Incidents in China: A Systematic Analysis Using ISM-DEMATEL Method

Escalator-related incidents (EIs) have recently resulted in serious injuries and even deaths. Given the frequency and severity of EIs, a systematic exploration of factors influencing EIs is critical in order to identify preventive measures. Twenty-two factors influencing EIs were identified by analyzing 213 EI cases in China and related literatures. A combination of the Interpretive Structural Modeling (ISM) and Decision Making Trial and Evaluation Laboratory (DEMATEL) methods were utilized to establish a hierarchical structure of the influencing factors and to distinguish cause factors and effect factors. The results show: (i) behavior, emergency plan, safety rules, safety supervision, information exchange, safety culture, and safety education are the most important factors influencing EIs; (ii) safety education is the cause factor imposing the greatest influence on other factors while behavior is the effect factor that is the most influenced; and (iii) the structure of influencing factors has five hierarchies, and factors in the root cause layer are settings and components, safety rules, safety supervision, safety culture, and safety education. Management priority should be given according to the hierarchy level, and the interaction of factors should be considered when taking preventive measures. The corresponding five-layer countermeasures are proposed to reduce escalator-related injuries.

Escalator-related injuries in one of the deepest subway stations in Europe

BACKGROUND

Escalator-related injuries (ERI) have emerged as a new injury type due to the frequent use of escalators in Metro stations.

OBJECTIVES

Investigate ERI in the stations on the Marmaray metro line.

DESIGN

Retrospective, observational study.

SETTING

Patients admitted to the emergency department of a training and research hospital.

PATIENTS AND METHODS

All patients with ERI were included in the study. We analyzed demographic characteristics, injury type and anatomical location of injury, Glasgow coma score, and body mass index (BMI). Patients were grouped by BMI: underweight (BMI less than 18.5 kg/m2), normal weight (BMI=18.5-24.9 kg/m2), overweight (BMI=25-29.9 kg/ m2) and obese (BMI greater than or equal 30kg/m2).

MAIN OUTCOME MEASURES

Injury characteristics and BMI values of patients with ERI.

SAMPLE SIZE

82 patients.

RESULTS

The mean age was 45.1 (15.5) years (range:14-77 years). Forty-two were women (52.5%). The mean BMI was 26.7 (2.2) kg/m2 (range: 22.1-33.3 kg/m2)]. Most of the patients who were injured due to escalators were older than 50 years (n=39, 47.6%) and 77.5% (n=62) of all patients were overweight. There was a significant relationship between increased BMI and serious ERI (P=.010, OR: 1.85, 95% C.I: 1.132.65). The most frequent mechanism of injuries was a fall (97.6%). The majority of injuries were the head (42%) and extremity injuries (33%). The major type of ERI was soft tissue injuries (41.3%), followed by lacerations (20.7%), closed head injuries (18.5%), fractures (15.2%) and serious injuries (4.4%). Serious injuries were more prevalent in patients aged older than 50 years (P less than .05), and in overweight and obese individuals (P less than .001) CONCLUSION: Novel protective measures against ERI should be developed for crowded subway stations.

LIMITATIONS

The small sample size and retrospective nature.

CONFLICT OF INTEREST

None.