Accident Trends Research Articles

Temporal trend and epidemiological profile of accidents involving venomous animals in the State of Rio de Janeiro, Brazil (2012-2021)

Introduction: Accidents involving venomous animals have significant clinical implications. In Brazil, accidents caused by scorpions (59%), spiders (13.6%), and snakes (10.9%) are the mostfrequently reported, although proportions vary by region. This study describes the temporal trends and epidemiological characteristics of accidents involving venomous animals in the State of Rio de Janeiro. Methods: This is an ecological (time series) study of accidents caused by venomous animals over the last 10 years (2012 to 2021) in Rio de Janeiro. Data were collected from the Brazilian Notifiable Diseases Information System. We estimated incidence rates and analyzed time series using Joinpoint regression, according to geographical region, age, and type of animal. Results: From 2012 to 2021, 17,891 accidents involving venomous animals were reported, with rates increasing from 8.4 to 10.7 per 100,000 inhabitants. Snakes were responsible for the highest number of cases (33.4%), followed by scorpions (27.2%) and spiders (27.0%). Snakebite cases remained stable, while cases of arachnidism and scorpion envenomation increased, marking them as the main types of accidents. The regions with the highest rates were Centro-Sul (65.5 per 100,000 inhabitants), Baía da Ilha Grande (62.3), Médio Paraíba (45.3), and Serrana (43.5). Males and the 40 to 59 age group predominated, but all age groups showed an increase, with a more pronounced rise among children and adolescents. Most accidents were classified asmild, although 51 deaths were recorded.Conclusions: Rio de Janeiro has relatively low rates of accidents involving venomous animals,but some regions have values higher than the national average. Snakebite accidents remainedstable, except in the adolescent group. Accidents involving scorpions and spiders showed anupward trend.

National Trends and Patterns in Traffic Road Accidents in Pakistan: A Statistical Analysis

National Trends and Patterns in Traffic Road Accidents in Pakistan: A Statistical Analysis

Two Decades of Fatal Workplace Accidents in Milan and Monza, Italy: Trends, Work Sectors, and Causes From Autoptic Data.

Work-related fatalities represent an important global public health threat, accounting for over 300,000 deaths annually. Despite preventive strategies, fatalities persist, necessitating comprehensive investigations. Autoptic investigations, when ordered, offer comprehensive data on fatal accidents, in particular with detailed information about the type of accident, lesions, and type of work, enabling a thorough analysis of various factors contributing to workplace deaths. This study investigates work-related fatalities in Milan and Monza Brianza, Lombardy, analyzing patterns, industries, and factors leading to death among workers. This retrospective study analyzed all fatal work accident data from the Autopsy Database of the Legal Medicine Institute of Milan from January 2000 to December 2022. We also estimated the risk of fatal accidents per 1.00.000 workers using ISTAT data for the different work sectors. Of the 17,841 violent deaths in Milan and Monza Brianza, 308 resulted from work-related accidents. The majority were male (95.1%). The secondary sector showed the higher estimated risk (27 /106 vs. 19/106 in primary and 2.3/106 in tertiary sector) accounted for 78.5% of fatalities, with specialized workers (35.1%) and construction workers (25%) being the most affected. Falls from above were the leading cause of death (36.7%). A decreasing trend in fatal accidents over the study period was observed, with July having the greatest frequency. The most common causes of death were polytrauma (36.4%) and head injuries (19.2%). Non-Italian workers constitute a noteworthy percentage of fatal cases (24%). The secondary sector has the most fatal accidents at work despite a decrease in accidents over the observed time period. There is a need for rigorous analysis and interventions, using forensic autopsy case data to help understand causes, and collaboration between institutions is encouraged to develop effective preventive policies.

TIME SERIES MODELLING OF ROAD TRAFFIC ACCIDENTS IN WEST ARSI, ETHIOPIA

The aim of this research is time series modeling of road traffic accidents in the west Arsi zone, Ethiopia, it focused on monthly traffic accidents from January 2016 to December 2020. The goal of this study was to explore the number of traffic accidents to fit a time series model for the monthly number of road traffic accidents and to forecast a tow year ahead of the number of road traffic accidents. The analysis was done by using statistical software packages using this software and knowledge of time series analysis, trend, ACF, PACF, and Box-Jenkins analysis were computed. From the trend plot, the road traffic accidents was fluctuate from month to month as well as from year to year. There was total accident fluctuation from month to month (not stationary) a total of 1010 RTA were observed. The mean of 16.83 and standard deviation of 5.764 for the total accident was served the minimum and maximum record of road traffic accidents is 4 and 33 respectively. By differencing data one time, (2, 1, 3) model was fitted for making a two-year ahead forecast. Proper model adequacy checking was done. Two-year ahead forecasts showed that October, January, and April 2021 are the months with the most prominent values. Even if the trend in total accidents was decreasing there is still a need to pay more attention in order to prevent the occurrence of accidents related to road traffic accidents.

Trends in Accidents Involving Healthcare Workers in the Pre-Pandemic and Pandemic Periods: Injury Notifications from an Emergency Department

Trends in Accidents Involving Healthcare Workers in the Pre-Pandemic and Pandemic Periods: Injury Notifications from an Emergency Department

Analysis of the spatial distribution and future trends of coal mine accidents: A case study of coal mine accidents in China from 2005–2022

A scientific grasp of the macro law of coal mining accidents can contribute to strengthening their prevention and control and guaranteeing a stable energy supply. In this study, 2,269 investigation reports of China's coal mining accidents from 2005 to 2022 were adopted as the basic data source, and GIS spatial analysis and rescaled range analysis methods were utilized to comprehensively reveal the spatial-temporal distribution features, and evolutionary patterns of coal mining accidents in China. The findings indicate that the numbers of gas explosion, permeability, outburst, suffocation and roof fall accidents has rapidly declined. The coverage area of coal mining accidents has gradually moved toward western of China. However, the center of the area covered by coal mining accidents during the study period was mainly concentrated in Shanxi and Henan Provinces. Besides, the number of deaths resulting from coal mining accidents across the country has gradually decreased, while the time series exhibited high continuity, with future changes consistent with past changes. The average cycle period of the coal mining accident sequence was 5 years. Through the systematic analysis of coal mine accidents conducted in this research, the law of accident occurrence was more comprehensively revealed, providing a reference and basis for the government and enterprises to implement precise preventive measures.

Determining the Safety Level of State Roads: An Italian Case Study

This study aims to establish an effective approach for evaluating the safety performance of road infrastructure. Road safety levels are typically quantified using safety performance indicators. However, due to the stochastic nature of accidents, many safety performance indicators cannot adequately and completely describe reality. Therefore, predictive methods based on regression models are widely used. This approach also allows for the identification of latent risk conditions in the infrastructure, even in the absence of accidents. Among available approaches, the Highway Safety Manual (HSM) methodology is chosen for its synthesis of validated highway research and best practices for incorporating safety into both new design and rehabilitation. For this study, a preliminary new version of HSM is used. The application of this method, which combines a predictive model with observed accidents through an empirical Bayesian approach, requires a calibration process that is crucial to tailoring this method to the specific study context. In this research, the predictive model is calibrated for single carriageway roads with one lane per direction across the Italian national network. Following calibration, the safety indicators are evaluated. The results obtained according to different indicators are compared to show the importance of adopting this method to counteract the regression to the mean of observed crashes. In fact, the method, supported by empirical Bayesian analysis, enables the identification of high-risk sections of the road network, selecting more sections that would be neglected by traditional indicators based solely on observed crashes. Finally, a possible approach to prioritizing sites for inspection based both on the excess of crashes and the Safety Potential (SAPO) is proposed. In addition, SAPO is adjusted to local conditions to account for the specific context and the decreasing trend of accidents over the years.

Causes and Trends of Motorcycle Accidents

Road safety has long been considered as one ofthe social responsibilities to the Malaysian Government. Inthe visibility of this responsibilities, multiple bodies concernon road safety has been formed within the governmentdepartments, private agencies, and voluntary organizations.The current study was carried out to understand the recenttrend and statistics of road motorcycles in Kajang,Malaysia. The statistics for road accidents and the relatedcasualties in Malaysia from 2005 to 2008 was obtained fromthe Bukit Aman Police Headquarters. The statistics ofregistered motor vehicles in Malaysia will be provided byRoad Transport Department of Malaysia. Data will beanalyzed using SPSS software. Descriptive and hypothesistesting techniques were used to obtain the results based onthe stated objectives of the study. From the crash statistics,it can be revealed that Kajang has a relatively higherincidence of casualties (dead, seriously injured and lightlyinjured). In addition, the results show that the young ridersof motorcycles among the age of 16 to 20 years from themajority of those who die in motorcycles accidents.Moreover, it is recognized that the considerable percentageof the motorcyclists and the pillion riders did not wearsafety helmets when they were traveling on the roadsnetwork. The Malaysian government has to organize manymotorcycle safety campaigns in the coming few years.

Analyzing the trend of mortality due to traffic and nontraffic accidents: a study in the north of Iran

Background: Examining the raw and standardized mortality rates is the primary strategy for improving life expectancy and human health in society through identifying risk factors and dealing with the factors that cause them. Objectives: This study examined the trend of mortalities due to traffic and nontraffic accidents. Methods: This cross-sectional study examines the registered mortalities during the years 2016–2022 in the form of a census in the health department’s death registration and classification system. Mortality was analyzed as raw and standardized mortality in each 100 000 population. Results: Out of 18 265 deaths during 2016–2022, 1305 (7.15%) were related to accidents and incidents. The age-standardized total mortality rate in the first year was 32.9, and in the final year of the study, it was 33.3 per 100 000 people; although there are fluctuations, this trend is upward (P.trend=0.021). Also, the age-standardized traffic death rate in the first year is 19, and in the final year is 12 per 100 000 people; this decreasing trend was not statistically significant (P.trend=0.061). The incidence of age-standardized intentional nontraffic deaths was 1.7 in the first year of the study interval and 9.8 in the last year in 100 000 people, which showed an upward trend (P.trend<0.001). Conclusion: In light of the generally increasing trend of accidents, especially nontraffic accidents, universal and well-rounded measures are necessary for safety matters and reducing mortality.

Statistical Analysis of Occupational Fatal Accidents for Risk Assessment

Abstract According to Malaysia's Department of Occupational Safety and Health (DOSH), workplace accidents have increased dramatically as the country's economy has grown rapidly. The purpose of this study is to identify the trends that lead to fatal workplace accidents in Malaysia. This study investigated 505 fatal accident instances from fifteen industries, using both qualitative and quantitative methodologies. The DOSH discovered and reported these fatalities (2010-2020). Data was organized and coded, then analyzed using Spearman's rank order correlation, frequency analysis, Eta squared, Cramer's V, and Chi-square. Moreover, neurolinguistic programming was used to generate the word cloud and sentiment analysis. Furthermore, light gradient-boosting machine learning was used to better understand the causes of fatalities. The outcomes indicated that fatal falls from heights were the leading cause of fatal accidents (32%). General laborers were the most susceptible to fatal accidents (60%). Contract workers were more likely to die in construction accidents (64%). The findings of this study gave useful insights into workplace fatal accident trends and preventive measures throughout sectors.

ANALYSIS OF ACCIDENT DATA AT PT X FOR THE 2018-2022 PERIOD USING THE HFACS-MINING INDUSTRY FRAMEWORK METHOD

The mining industry is a high-risk industrial activity. Human factors have been identified as the most common cause of major accidents in the mining industry. The Incident Management System of PT X, which is a company in exploration, mining, processing, and marketing sector of copper, gold and silver concentrate, part of the state-owned mining holding enterprise Mining Industry Indonesia, documented 322 cases of accidents within the 2018-2022 period. This research aims to analyze accident data at PT X using the Human Factor Analysis and Classification System-Mining Industry (HFACS-MI) framework. Methods. This research collected qualitative data for 322 accident cases at PT X in 2018-2022 from the incident management system database categorized as recordable injuries. Factors causing the accident were coded using the HFACS-MI framework. Accident data analysis used descriptive statistics. Results. The study findings revealed that 84% of all accidents involved contractor workers and 16% involved permanent workers at PT X. The results of the analysis using the HFACS-MI framework showed that each layer or level contributes to accidents, namely external factors by 44%, organizational influence by 68%, unsafe leadership by 90%, preconditions for unsafe acts by 99%, and unsafe acts by 99.7%. Conclusion. These findings emphasize the need to reduce the number of human errors during mining operations to reduce the current accident trend. The HFACS-MI framework has proven to be a valuable tool for robust accident analysis of human factors in mining.

A novel unmanned aerial vehicle driven real-time situation awareness for fire accidents in chemical tank farms

A large number of flammable hazardous materials are stored in chemical tank farms, where fire-induced domino accidents can be easily triggered. In this study, a novel real-time fire situation awareness (FSA) approach based on UAV is proposed to capture spatio-temporal evolution characteristics and predict development trends of fire accidents. Firstly, fire images are acquired by UAV, and the key parameters of fire are extracted in real time based on YOLOv8 network. Then, the thermal radiation and impact on surrounding equipment are predicted by combining LSTM network, solid flame model and improved probit model. The proposed method is verified by small-scale tank fire experiments, which demonstrate its superiority in terms of physical consistency and prediction accuracy. The results show that the mean absolute percentage error (MAPE) of fire parameter extraction is not higher than 5.43%, the MAPE of thermal radiation prediction is not higher than 25%, and the dynamic time to failure (dttf) for the model tank at different location is predicted. This work has the potential to provide a novel solution for real-time assessment of fire size and trend prediction to support firefighting, emergency rescue and decision making in fire accident scenarios.

Accidental Survey of Different Intersection of Junagadh City

Abstract: Intersections are known to be high-risk areas for traffic accidents due to the convergence of different streams of traffic. By conducting an accidental survey, identify patterns and common causes of accidents. This study attempts investigation and analysis to prevent accidents at intersection which have high number of accidents. To get an insight into the trend of road accidents we collect last 3 years of accident data of different intersection of Junagadh city from District Traffic Office of Junagadh by taking permission from SP office Junagadh. And selected intersection is “Majevadi gate” Because in last 3 years, maximum number of accidents have occurred at this intersection. To reduce accidents at this intersection, we design a rotary intersection at the Intersection.

Pattern recognition of urban traffic accidents: Application of cluster analysis and support vector machine in accident pattern recognition

Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones.

Review of Road Traffic Accident Burdens in Transportation Sector of Ethiopia

The transportation sector has a very immense role in the economic development of a given country. Because human beings' day-to-day activities re highly depends on the transport service. But this sector has its own drawbacks, like road traffic crashes and environmental pollution. In Ethiopia, the road transport sector is poor in terms of quality and service delivery systems. Due to this, the frequent occurrence of road traffic crashes has been recorded in the country continuously. To that end, this paper was mainly intended to review road traffic accident burdens in the transportation sector of Ethiopia. In detail, reviewing the costs of road traffic accidents, the causing factors of road traffic accidents, the types of vehicles highly involved in road traffic accidents, and the victim groups of communities affected by road traffic accidents at the national level were the specific objectives of this paper. Meanwhile, all road traffic accident data found in Ethiopia from the federal police commission report papers, journal articles, and dissertation papers were used for analysis purposes. Besides, the collected data is presented in tables and figures. Furthermore, the data was analyzed and interpreted based on a descriptive research design. Finally, the result of this review shows that trends of road traffic accidents in Ethiopia have been increasing over time by killing, injuring people and damaging resources. Drivers’ bad behaviors, low know-how of road users, poor road infrastructure, old vehicle conditions, and environmental factors are aggravating the occurrence of road traffic accidents in the country. Thus, to tackle this problem, educating people, enforcing traffic laws, enhancing engineering measures, and giving emergency responses were the recommendations given in this paper.

Analysis of road traffic accidents and casualties associated with electric bikes and bicycles in Guangzhou, China: A retrospective descriptive analysis

IntroductionElectric bicycles (e-bikes) and bicycles in large Chinese cities have recently witnessed substantial growth in ridership. According to related accident trends, this study analyzed characteristics and spatial distribution in the period when e-bike-related accidents rapidly increased to propose priority measures to reduce accident casualties. MethodsFor e-bike- and bicycle-related accident data from the Guangzhou Public Security Traffic Management Integrated System, linear regression was used to examine the trends in the number of accidents and age-adjusted road traffic casualties from 2011 to 2021. Then, for the period when e-bike-related accidents rapidly increased, descriptive statistics were computed regarding rider characteristics, illegal behaviors, road types, collision objects and their accident liability. One-way analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test. P < 0.05 was considered statistically significant. Finally, the density distribution of accidents was presented, and Moran's I (MI) was used for assessing spatial autocorrelation. Hotspots were identified based on an optimized hotspot analysis tool. ResultsBetween 2011 and 2021, the number of accidents and casualty rate (per 100,000 population) increased for e-bikes but decreased for bicycles. After 2018, e-bike-related accidents increased rapidly, and bicycle-related accidents plateaued. Accident hotspots were concentrated in central city areas and suburban areas close to the former. Three-quarters of accidents occurred in motorized vehicle lanes. Most occurred on roads without physically segregated nonmotorized vehicle lanes. More than three-fifths of the accidents involved motor vehicles with at least four wheels. The prevalence (per 100 people) of casualties among e-bike rider victims and cyclist victims accounted for 92.0 % and 96.5 %, respectively. A total of 71.6 % of e-bike-related accidents involved migrant workers. Riding in motorized vehicle lanes was the most common illegal behavior. ConclusionsAlthough e-bike-related and bicycle-related accidents presented similar characteristics, the sharp increase in e-bike-related accidents requires attention. To improve e-bike safety, governments should develop appropriate countermeasures to prevent riders from riding on motorways, such as improving road infrastructure, adjusting the driver's license system and addressing priority control areas.

Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)

Abstract The objective of this study is to analyse the spatial-temporal patterns of traffic accidents using data from 2011 to 2020 for the AP Vojvodina. The spatial-temporal patterns of traffic accident hotspots were identified at the municipality level using the Getis–Ord G i ⁎ {G}_{i}^{\ast } statistic in ArcGIS Pro software. Trends in traffic accidents were analysed using the Mann-Kendall (MK) statistic. A certain trend in the number of traffic accidents, fatalities, and injuries was detected in 15 out of 45 municipalities. In two municipalities, the trend in the number of traffic accidents is decreasing, while in seven, it is increasing. In three municipalities, a decreasing trend in the number of traffic fatalities was observed. A decreasing trend in traffic injuries was identified in five municipalities. Hotspot analysis on the number of traffic accidents and traffic injuries revealed hotspots in seven municipalities, while no hotspots were detected in the number of traffic fatalities. In the traffic casualties weighted number, persons in tractors, hotspots were identified in two municipalities. This research has the potential to enhance traffic safety by directing targeted safety measures towards identified hotspots. Such measures could ultimately lead to a reduction in traffic accidents, consequently curbing the number of both fatalities and injuries.

Investigation and analysis of ship to platform collision incidents on the UK continental shelf: highlighting trends between the enforcement of offshore regulations and the occurrence of vessel to platform collision incidents

An offshore installation is exposed to ship collision risk from in-field and passing vessels. Both categories of collision have occurred on the United Kingdom Continental Shelf (UKCS), and both have the potential to result in catastrophic damage to the installation, although to date only significant consequences have been observed in UK waters. World-wide, catastrophic collisions with installations have occurred resulting in severe damage to vessels and installations, leading to loss of life and environmental damage. This led to the authors investigate the occurrence and reporting of ship to platform collisions on the UKCS 1971–2017. It was found that there were periodic fluctuations in the occurrence of collision accidents. This research analyses the accident trends in order to determine the whether the fluctuations were a regular occurrence and, if so, what was the cause. 511 ship to platform collision incidents between 1971 and 2017 were subject to analysis, in terms of damage classification, the vessel type, month of occurrence and a comparison of the release of regulations with the fluctuation of incidents. Subsequently, this analysis has identified a key trend between the reporting of offshore collision incidents and the release and enforcement of offshore Safety Case regulations.

IJCM_49A: Mortality trends of road traffic accidents due to human errors in Chhattisgarh and India (2016-2022)

Background: Road safety continues to be a major developmental issue, a public health concern and a leading cause of death and injury affecting not only the victims and their family but also the economy as a whole in terms of untimely deaths, disabilities and loss of potential income. India ranks 2nd in the number of road accidents and 1st in mortality due to RTA in the world. Road accidents are multi-causal due to interplay of various factors. These can be broadly categorized into those relating to (a) Human error (b) Road condition/ environment (c) Vehicle condition. Human errors for RTA include non-compliance to traffic rules and not using helmets and seatbelts, resulting in mortalities which can be mitigated by proper education, adherence and implementation of traffic rules. Objective: To analyze the mortality trends due to RTA by human errors in Chhattisgarh and India 2016-2022 Methodology: The RTA mortality statistics were retrieved from the Road accidents in India report published annually by the Ministry of Road Transport and Highways. A literature search was done in electronic databases like-PubMed, Google Scholar, other platforms using keywords- RTA mortality, Distracted driving Results: From 2016 to 2022, both Chhattisgarh and India experienced a steady rise in fatalities, peaking in 2022 due to overspeeding and helmet non-compliance. In 2019, India saw its peak in fatalities from alcohol-related driving, mobile use, and signal jumping, followed by a decline. Chhattisgarh experienced the highest signal jumping fatalities in 2017 and alcohol-relatedriving deaths in 2018 compared to other years Conclusion: The study reveals a decrease in road traffic accident fatalities caused by certain human errors post-2019, credited to the adoption of the Motor Vehicle Amendment Act 2019. Boosting public awareness and establishing robust surveillance measures are imperative in curbing fatalities caused by road traffic accidents (RTAs)

Outcome of Seatbelt Education and Safety Program Among Teenagers.

Despite the effectiveness of seatbelts, concerns persist about compliance, especially among teenagers. Survey data from a local high school and registry data from a level 1 trauma center were used to observe seatbelt and motor vehicle accident trends. The survey data was analyzed to gauge student's sentiments on seatbelt education. The trauma center data was analyzed to identify characteristics and trends among teenage motor vehicle accidents. Social media was the most common strategy selected for seatbelt safety awareness. Random seatbelt checks performed over 4 months revealed seatbelt compliance rates of 90%, 93.55%, and 96.94% after education intervention. Trauma center data showed that lack of seatbelt usage resulted in greater morbidity. These findings emphasize the need for targeted interventions. This study provides insights into creating effective education campaigns that can be used to enhance safety belt compliance and potentially reduce injury.