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

Epidemiological characteristics and factors influencing hospitalization burden among trauma patients: a retrospective analysis

PURPOSE

This investigation aimed to understand the epidemiological characteristics and hospitalization burden and its possible influencing factors of patients with different injury mechanisms.

METHODS

All trauma patients admitted via the emergency department at a trauma center from November 1, 2020, to April 30, 2022, were identified. The hospitalization burden, including the number of hospitalizations, deaths and in-hospital mortality, length of stay (LOS), and medical costs, was calculated. Univariate and multivariate logistic regression models were used to analyze the factors influencing the hospitalization burden of trauma. The receiver-operating characteristic (ROC) curves were drawn to evaluate the predictive value of the multivariate model.

RESULTS

16 485 trauma patients with 16 552 hospitalizations were included, with an in-hospital mortality rate of 1.269‰, median LOS of 7 days, and median hospitalization costs of 54 725.28 CNY. The median age was 52 years. 62.54% were hospitalized due to falls. The upper and lower extremities were the most common injury regions. There are differences between the demographic, injury, and hospitalization characteristics and factors influencing hospitalization burden across injury mechanisms, but there were also common influencing factors. Injury region, surgery, transfusion, and ICU treatment are influential factors for prolonged LOS. Age, injury region, surgery, and transfusion were influential factors for high hospitalization costs.

CONCLUSIONS

This study provided primary evidence on the hospitalization burden of trauma. Considering demographics, injury and hospitalization characteristics as additional discriminators could further intervene in LOS and medical costs. Targeted efforts to use more early prevention measures could potentially lower future hospitalization burden.

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.

Analysis on Causative Factors and Evolution Paths of Blast Furnace Gas Leak Accident

Although the interest in metallurgical accident investigation of blast furnace gas (BFG) leakage has increased to explore the engineering failures, more effort is needed to address the individual and organizational causative factors to clear and determine the weak links for improving safety management and accident prevention to achieve green metallurgical manufacturing. This study aims to examine the causative factors and evolution paths of BFG leakage by introducing a combined method, the 24 model and Bayesian network (BN), based on 50 cases of fire, explosion and suffocation accidents caused by BFG leakage. A BN model of BFG leakage was established based on the identification of 25 causative factors by the 24 model. Results showed that eight nodes, including A1 (unsafe operation), A2 (unsafe behavior), A4 (unsafe condition), B1 (valve failure), B2 (improper gas safety operation), X4 (use of BFG violates regulations), X5 (water gas is not cut off before shutdown reduction) and X6 (incomplete steam purging), were more sensitive than others, and the posterior probability of nodes A1, A2, A3 (unsafe command), A4, B1, B2, B4 (improper emergency behavior), B5 (unsafe behaviors on BFG site) increased compared to prior probability. Three main accident causal chains were obtained which indicate that control the unsafe operations (A1) related to gas (B2) and valve (B1) are suggested to be improved. Another important factor is A4 (unsafe condition), which is related to intrinsic safety conditions. Considering the results, the key points of 3E strategy about BFG leakage prevention are suggested. This study provides useful insights to understand the organizational and individual factors and their relative influence in BFG leakage accidents, which will support BFG leakage prevention and safety management.

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.