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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.

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.

New York City is the most populous city in North America and the fourth most populous in the world. Due to the high population density and significant commuting population, the city experiences a large number of vehicles operating in a congested environment, leading to a substantial number of traffic accidents. This study examines a dataset compiled by the New York Police Department, which records every major vehicular accident in New York City from 2013 to 2023, exploring aspects such as accident types, severity, causes, and locations. This period includes the COVID-19 pandemic and other external factors like fluctuating gasoline prices, the rise of for-hire vehicle (FHV) services, and vehicles with new safety features. Data from multiple sources are analyzed to understand how these factors impacted accident rates during this timeframe. The analysis shows that the COVID-19 pandemic significantly reduced accidents due to decreased motor vehicle traffic, with post-pandemic accident rates remaining at less than half of pre-pandemic levels. This sustained decline correlates with reduced traffic, increased FHV usage over taxis, and a growing number of new vehicles with advanced safety features. This study uses these datasets to develop a mathematical model to quantify these correlations and to provide insight for urban planners and policymakers seeking to improve road safety and manage traffic flow.

In actual traffic accidents, a phenomenon is observed where the speedometer needle jams at a certain value after the impact. Using the residual reading of the speedometer, we may infer the approximate vehicle speed while the impact occurred. Based on the function of typical speedometer, the impact characteristic and damage mechanism of vehicles, this paper describes the research of how the speedometer needle stops during accidents and its failure modes. According to the statistics of thirty specific traffic accident cases containing residual speedometer needle readings, we obtain and summarize the characteristics and rules concerning the accidents and the related information. Furthermore, a typical accident case is analyzed by in-depth level. The results reveal that for the accident vehicles with electronic speedometers, there is a strong correlation between the residual reading of the speedometer and the real vehicle impact speed. The conclusions may provide a new effective method and reference for accident reconstruction and the estimation of vehicle speed.

Zusammenfassung Hintergrund Der Notarztindikationskatalog basiert auf veralteten Studien und gibt wenig Anhalt für Alarmierungskriterien nach Verkehrsunfällen. Fortschritte der Fahrzeugsicherheitstechnik und Veränderungen der verfügbaren Ressourcen machen eine Reevaluation der Indikationen notwendig. Ziel dieser retrospektiven Registerstudie ist die Identifizierung von präklinisch erfassbaren Unfallvariablen für schwere Verletzungen nach Verkehrsunfällen. Methodik Im Zeitraum 01.01.2000–31.12.2021 wurden 47.145 Verunfallte anhand der GIDAS-Datenbank eingeschlossen. Separate Datensätze für Schwer- (AIS 3+) und Leichtverletzte wurden ausgewertet. Ergebnisse Herausschleudern (PPW 80,6 %), Einklemmung (PPW 75,6 %), brennende Fahrzeuge (PPW 57,1 %), problematische Rettung (PPW 56,3 %), Fahrzeugzerreißung (PPW 51,6 %) und Amnesie (PPW 50,3 %) wiesen auf schwere Verletzungen bei Fahrzeuginsassen hin. Bei ungeschützten Verkehrsteilnehmern (Motorrad‑, Fahrradfahrende, zu Fuß Gehende) wurden auch Helmverlust (PPW 61,1 %), Überrollen/Mitschleifen (PPW 41,9 %), Scheibenbruch am Gegnerfahrzeug (PPW 35,8 %) und Folgeanprall mit Objekten (PPW 31,1 %) identifiziert. Der Chi-Quadrat-Test zeigte signifikante Assoziationen zwischen den Variablen und schweren Verletzungen. Kombinationsvariablen erreichten PPW-Werte über 82 %. Diskussion Der Notarztindikationskatalog liefert kaum präklinisch feststellbare Kriterien und sollte anhand der objektiven Registerdaten überarbeitet werden. Abfragemodelle für Leitstellendisponenten sollten getestet werden.

Data on occupational accidents are usually stored in large databases by worker compensation authorities, and by the safety and prevention teams of companies. An analysis of these databases can play an important role in the prevention of accidents and the reduction of risks, but it can be a complex procedure because of the dimensions and complexity of such databases. The SKM (SOM K-Means) method, a two-level clustering system, made up of SOM (Self Organizing Map) and K-Means clustering, has obtained positive results in identifying the dynamics of critical accidents by referring to a database of 1200 occupational accidents that had occurred in the wood industry. The present research has been conducted to validate the recently presented SKM methodology through the analysis of a larger data set of more than 4000 occupational accidents that occurred in Piedmont (Italy), between 2006 and 2013. This work has partitioned the accidents into groups of different accident dynamics families and has quantified the severity and frequency of occurrence of these accidents. The obtained information may be of help to Company Managers and National Authorities to better address preventive measures and policies concerning the clusters that have been identified as being the most critical within a risk-based decision-making framework.

Work system safety is a function of many factors, besides it is dynamic and complex. There may be relations and dependencies among the safety factors. Therefore, work system safety should be analyzed in a holistic manner. In this study, the accidents data analysis is used to identify the links among the factors; the relationship analysis of the independence between the behavioral factors has been performed in order to find the non-independent factors. Among the Human Factors Analysis and Classification System (HFACS), the impact of the high-level factors on the bottom level is determined. After a period of monitoring and rectification, it implements a new round of safety monitoring and factors comparison and interaction analysis, so to achieve the goal of “the nature of safety” of hydropower projects construction.

Analysis of accidents has found driver distraction to be a significant cause of accidents on the highways [1]. Therefore, studying the causes of driver distraction that impact its risk level is needed for a better understanding of accident occurrences. There is general scarcity of research in this field with no established research framework to study driver distractions. This paper proposes a modular research framework for conducting a driver distraction study on Polish city highways. The framework contains guidelines for distraction studies for wide range of cost and time intervals such as a quick, low cost study like analysis of existing accident databases maintained by the cities to relatively higher cost, longer duration study involving field data collection, statistical modeling, and analysis. A city may choose one or more modules to suit their study requirements including statistical and simulation tools to assess and validate the historical or empirical result. The framework is based on the careful modifications and revisions of an earlier transit bus driver study conducted in the Commonwealth of Virginia, U.S.A., and results from this research are presented for purposes of illustration.

The number of recognized accidents during agricultural work is still very high in Austria. In the years 2008 to 2009, there occurred 84 approved work accidents with mowing machines. The main causes of accidents were the loss of control of machines, transportations or conveyances, hand tools, objects or animals. In the literature, numerous studies of general agricultural and forestry accident situations are available. Detailed studies on specific types of agricultural machines, which describe concrete circumstances and causes of accidents, are in limited numbers. The accident database from the General Accident Insurance Institution and the Austrian Social Insurance Institution of Farmers, with personal and accidental data information about mowing machine accidents, were analyzed. The results showed that most accidents occurred on mixed agricultural farms (68%). The majority of the injured persons were male (86%), over 40-years-old (86%) with an agricultural or forestry education (91%). The most common accidents occurred in the summer months (69%) and on afternoons during the working week (79%). The majority of accidents were caused by contact with the machine (55%) and the loss of control (73%) during their operation (60%) and harvesting work (63%). The most frequently injuries were wounds, fractures and superficial injuries (81%) to the upper and lower extremities (66%). The results of the chi-square test showed significant correlations between the specific task with the form of contact, the working process, the day and season. Results of the odds ratio determination showed an increased risk of suffering serious injury for men in the first half of the year and half of the day due to loss of control over the machine during agricultural harvesting work.

This paper compares the distraction risks and factors causing such distractions between regional and urban transit bus drivers. The objective is to ascertain if the nature and intensity of driver distraction and associated distraction factors are common at the different types of transit agencies. To establish this, an independent driver distraction study was conducted at a regional transit agency and an urban transit agency located at different areas in the Commonwealth of Virginia, USA Using accident databases and bus driver surveys, the distracting activities were classified into risk zones according to their severity. Furthermore, multinomial logistic regression models were applied to establish relationships between a set of dichotomous and continuous distracting factors and intensity of the multi categorical levels of driver distraction based on risk zones. Among the useful findings was similarity in the highest risk distracting activities for regional and urban transit bus drivers which were mainly due to passengers, pedestrians and other road users. While many of the distraction related factors such as the service area (regional/urban), driver attributes (age, gender, driving experience, educational level, marital status etc.), driving pattern (driving schedule, driving hours per week, service location etc.), and type and age of the buses were significant in either regional and urban models, there were few that impacted both the transit agencies concurrently. The reasons for the resulting differences could be due to significant variations in driver attributes, driving pattern, type and age of buses between the transit agencies. Hence, training needs and policies to curb distracted driving may differ at both agencies.