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Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.

Project ownership is a critical factor that shapes safety management systems and accident patterns in construction. However, the Ministry of Employment and Labor (MOEL) industrial accident database, which is the largest construction accident database in Korea, does not include project ownership information. To address this limitation, this study developed a fine-tuned KLUE-BERT framework that automatically classifies project ownership using unstructured text fields (site name, client name, and workplace name) in MOEL data. Training data were constructed through manual classification of the 2018–2023 approved statistics and data augmentation. The proposed model achieved high classification performance. Multilayered statistical analyses were conducted using the classified 2014–2023 construction accident data across six key accident variables: accident type, accident cause, construction scale, accident severity, occupation, and worker tenure. The results revealed statistically significant associations between project ownership and all six variables. Public projects exhibited relatively high proportions of accidents involving construction machinery and vehicles, whereas private projects exhibited higher proportions of fall- and scaffold-related accidents. This study presents a novel artificial intelligence-based framework that generates analytical variables absent from the original data and demonstrates its utility through large-scale statistical analysis. The findings provide empirical evidence to support the development of project ownership-specific construction safety policies. Limitations include potential data leakage from pre-split augmentation and generalizability limited to Korean construction data.

Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles’ levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous’ versus Human-Driven Vehicles’ accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements. Through a matched case-control analysis this study reveals accident risk disparities between autonomous and human-driven vehicles. It suggests that accidents of vehicles equipped with Advanced Driving Systems generally have lower occurrence chance than human-driven ones in most scenarios.

Urban gas pipe network (GPN) is an important infrastructure to guarantee residents’ daily life. However, the risk of GPN has become increasingly prominent. Leakage is one of the biggest issues, which is easy to cause fire, explosion, poisoning, and so on. Therefore, the risk assessment of leakage is significant for the safety management of urban GPN. The main idea is to analyze the history accidents and predict the accidents that are happening. This paper explores to construct an integrated assessment model through Bayesian network (BN), Interpretive structural model (ISM), and expert evaluation method. First, the main risk factors of leakage and their coupling relationship are determined to increase the understanding of the complex system. Then, ISM is used to divide the logical network of factors to determine the hierarchical structure of BN. Finally, node probability is evaluated by Expectation–Maximization algorithm with the data collection of 89 real accidents. The model can be used to quantify the coupling strength and influence degree of each factor on the occurrence of leakage (the leakage that can easily lead to accidents, rather than small leaks). Then, the probability of GPN leakage can be predicted under a specific scenario. This study can provide a reference for safety management of GPN to reduce risk and potential loss.

South Korea is ranked as 4th among 34 nations of the Organization for Economic Cooperation and Development with 102 deaths in road accidents per one million population. This paper aims to investigate the factors associated with road accidents in South Korea. The rainfall data of the Korea Meteorological Administration and road accidents data of Traffic Accident Analysis System of Korea Road Traffic Authority is analyzed for this purpose. In this connection, multivariate regression analysis and ratio analysis with the descriptive analysis are performed to uncover the catastrophic factors involved. In turn, the results reveal that traffic volume is the leading factor in road accidents. The limited road extension of 1.47% compared to the 4.14% per annum growth of the vehicles is resulting in road accidents at such a large scale. The increasing proportion of passenger cars accelerate road accidents as well. 56% of accidents occur by the infringement of safety driving violations. The drivers with higher driving experience tend to have a higher accident ratio. The collected data is analyzed in terms of gender, driver experience, type of violations and accidents as well as the associated time of the accidents when they happen. The results indicate that 36.29% and 53.01% of accidents happen by male drivers in the day and night time, respectively. 29.15% of crashes happen due to safety infringement and violations of 41 to 60 years old drivers. The results demonstrate that population density is associated with the accidents frequency and lower density results in an increased number of accidents. The necessity of the state-of-the-art regulations to govern the urban road traffic is beyond dispute, and it becomes even more crucial for citizens' relief since in our daily lives road accidents are getting more diverse.

To solve the problems of difficult quantification of complex driving scenes and unclear classification, a method of complex measurement and scene classification was proposed. Based on the Bayesian network, the posterior probability distribution was obtained, the variable weights were determined by information entropy theory and BP neural network, and the gravitational model was improved so that the complex metric model of the driving scene was established, the static and dynamic complexity of the scene was quantified respectively, and a weighted fusion of the two was conducted. The K-means clustering method was used to divide the driving scenario into three categories, i.e., simple scenario, medium complex scenario, and complex scenario, and the rationality of the method was verified by experiments. This scenario complex metric method can provide a reference for studying the complex metrics and scene classification of smart vehicle test scenarios.

Nowadays, congestion and accidents are creating major risks to cities, including delays, higher fuel usage, and compromised safety. Effective traffic modelling and accident data analysis are critical for identifying high risk identifying accident-prone locations, understanding the causes of accidents and creating focused actions to enhance traffic flow and safety. GIS is an effective tool for integrating, analysing and visualizing different geographical data relevant to transportation networks such as, traffic flow, infrastructure, and safety. It enables geographical analysis and visualization of accident hotspots by integrating accident data, road conditions, traffic numbers, and environmental factors. The use of GIS in traffic modelling and accident data analysis provides considerable benefits in urban transportation planning and management. The aim of the paper is to provide an overview of the application of GIS in traffic modelling and accidental data analysis, highlighting the methodologies, advancements, and challenges in this field. The review shall provide a comprehensive assessment of the current state of traffic modelling and accidental data analysis using GIS. It will highlight the significant contributions of GIS technology, identify key research gaps, and offer insights into future directions for enhancing transportation planning and decision-making processes.

This study holds significant theoretical and practical importance for enhancing the safety management and operational efficiency of railway systems. Currently, there is a notable gap in standardized safety-level measurement methods across diverse railway operating entities. Conventional safety assessment approaches predominantly rely on qualitative analysis frameworks, which often fail to comprehensively address the multifaceted risk factors inherent in complex railway operating environments. To address these limitations, this study leverages historical operational data from participating railway companies to propose an advanced integrated quantitative methodology: the Global Safety Index (GSI)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This innovative approach quantifies railway safety conditions by systematically analyzing incident and accident data, integrating statistical modeling frameworks, data imputation algorithms, and comprehensive analytical protocols. The method enables detailed examination and interpretation of large-scale operational datasets within railway systems. The implementation of this quantitative framework has demonstrated substantial improvements in the accuracy of safety performance metrics. Furthermore, it offers robust technical support for developing risk mitigation strategies and optimizing safety performance in the railway sector. By employing systematic risk factor identification and data-driven safety quantification, this approach facilitates accident prevention, enhances risk early-warning systems, and provides evidence-based decision-making support. As research progresses and the accessibility of Chinese railway safety data increases, the analytical precision of this methodology can be further refined. Future applications may include in-depth analyses of Chinese railway risk event datasets, thereby offering strong technical support for the continuous elevation of safety standards in China’s railway operations.

With the growth of university chemistry experiment projects, the corresponding laboratory safety risks are increasing year by year for scientific research personnel, and specialized equipment. However, accident data are not stored systematically for lack of a safety platform to collect accident information, share the causes of accidents, and predict safety risks. To solve these problems, we designed a laboratory accident system to store and share related data, and predict risk levels. In this paper, the majority of chemistry laboratory accidents were manually collected by Python software (version 3.10.11) and were categorized based on their risk level. Moreover, the variable factors that generated risk were analyzed using Spsspro, which facilitates the construction of a meaningful forecasting model of laboratory safety via Stata. It is worth noting that the registered laboratory accident data in the proposed chemistry accident system were based on the data ownership safety architecture. The chemistry accident system can break through data barriers using confirmation and authorization key algorithms to trace non-tampered data sources in a timely manner when an emergency accident happens. Meanwhile, the proposed system can use our designed accident risk model to predict the risk level of any experimental project. It can also be recommended as an appropriate safety education module.

The purpose of this study is to analyze how the spatiotemporal characteristics of traffic accidents involving the elderly population in Seoul are changing by time period. We applied kernel density estimation and hotspot analyses to analyze the spatial characteristics of elderly people's traffic accidents, and the space-time cube, emerging hotspot, and space-time kernel density estimation analyses to analyze the spatiotemporal characteristics. In addition, we analyzed elderly people's traffic accidents by dividing cases into those in which the drivers were elderly people and those in which elderly people were victims of traffic accidents, and used the traffic accidents data in Seoul for 2013 for analysis. The main findings were as follows: (1) the hotspots for elderly people's traffic accidents differed according to whether they were drivers or victims. (2) The hourly analysis showed that the hotspots for elderly drivers' traffic accidents are in specific areas north of the Han River during the period from morning to afternoon, whereas the hotspots for elderly victims are distributed over a wide area from daytime to evening. (3) Monthly analysis showed that the hotspots are weak during winter and summer, whereas they are strong in the hiking and climbing areas in Seoul during spring and fall. Further, elderly victims' hotspots are more sporadic than elderly drivers' hotspots. (4) The analysis for the entire period of 2013 indicates that traffic accidents involving elderly people are increasing in specific areas on the north side of the Han River. We expect the results of this study to aid in reducing the number of traffic accidents involving elderly people in the future.