Incidence Of Accidents Research Articles

Research on Drivers' Behaviors of Heavy Goods Vehicles Based on Traffic Accident Analysis

Heavy goods vehicles (HGVs) have played a vital role in transporting goods. These goods are closely related to social development and people's lives, including daily necessities and parcels, and even industrial raw materials such as coal and steel. However, for many reasons, the number and incidence of HGVs traffic accidents remain high in all countries. Since HGVs have the characteristics of large volume, large mass and fast speed, the consequences of HGVs traffic accidents are often very serious, causing numerous property damages, and even seriously threatening people's lives. There are many factors that contribute to this tragedy, but it is worth noticing that HGVs drivers played a vital role. Their bad driving behaviors and habits may be to blame for the accidents. This paper identifies the bad driving behaviors and habits of HGVs drivers that may lead to traffic accidents, and analyzes the impact of these behaviors. This paper also attempts to establish the relationship between external factors such as insurance and drivers' driving behaviors, and explores the impact of public policies on traffic accidents. Finally, this paper draws the conclusion that many bad driving behaviors will lead to HGVs accidents, and several actions from drivers, government and companies can be taken to improve traffic safety. This paper adopts the field observation method, the interview survey method and the literature research method.

Analysis of The Effect of The Implementation of The Behavior Based Safety (BBS) Program By Company Management on Labor Behavior At PT. HYZ

HYZ Company is a goods shipping and logistics company in the maritime sector with a significant risk of work accidents, especially in the Occupational Health and Safety aspect. The company has implemented a Behavior Based Safety (BBS) program since 2011 to reduce the risk of accidents. This program is designed to increase safe behavior in the workplace and reduce accident incidents. Although BBS is known to reduce work accidents by up to 40-75%, HYZ Company has not conducted an in-depth analysis to determine the extent to which the implementation of this program affects the level of compliance with Occupational Health and Safety principles, including the use of personal protective equipment (PPE), safe work procedures and other safety practices. This study aims to evaluate the effectiveness of BBS at HYZ Company in the context of Occupational Health and Safety, focusing on changing workers' behavior and levels of compliance with safety standards. The results of this research can help companies strengthen their Occupational Health and Safety systems and reduce the risk of accidents while promoting a sustainable safety culture. With a better understanding of the impact of BBS on Occupational Health and Safety compliance, HYZ Company can take more effective measures to ensure worker safety and a safe working environment

Occupational accidents in Malta and the role of the occupational health and safety authority: A twenty-year analysis

The Occupational Health and Safety Authority (OHSA) was established in Malta in 2002. Since then, trends indicate that non-fatal accidents have decreased in Malta, while changes in fatal accidents are less clear. Since these trends have not been statistically investigated before, this study aims to do so. The study also aims to analyse the link between specific OHSA deterrent measures and changes in non-fatal accidents. A database compiled by the OHSA on the frequency of accident statistics in Malta and OHSA deterrent measures between 2002 and 2022 was analysed. The study demonstrated that the incidence of fatal and non-fatal accidents decreased significantly during the analysed period. The incidence of non-fatal accidents was more common in the transport and storage sector, the construction sector and the manufacturing sector. Fatal accidents were most frequent within the construction sector. Fatal accidents were common among the self-employed and foreign workers. Deterrents, especially those related to inspections and fines, were significantly associated with a decrease in fatal and non-fatal accidents. The study underscores those accidents have declined significantly since the establishment of the OHSA and demonstrates the benefits of specific deterrent measures. Continued focus is required on specific areas, including the construction sector, self-employed workers and foreign workers.

Analysis of the causes and safety countermeasures of coal mine accidents: A case study of coal mine accidents in China from 2018 to 2022

Statistical data on coal mine safety accidents in China from 2018 to 2022 were obtained. Mathematical statistical methods were used to analyze the accident level, accident type, death toll, time, space, and other factors. Grounded theory (GT), case-based reasoning (CBR), analytic hierarchy process (AHP), interpretative structural model (ISM) and other methods were used to analyze the causes of coal mine accidents in detail, and the characteristics, accident-causing attributes and importance of coal mine accidents in China were explored. The results indicate that (1) according to the level and type of accident, the incidence of general accidents in coal mines is the highest, with both the number of roof accidents and the death toll accounting for the highest values. From the time and location of the accidents, coal mine accident-prone areas are concentrated in northern and southern China. May and July are accident-prone periods every year. (2) Using GT-CBR to analyze the causes of coal mine accidents, 18 key statements were extracted, and 11 initialization concepts were summarized. Ultimately, the causes were categorized into 4 core categories: human factors (H), machine factors (M), environmental factors (E), and organizational management factors (O). (3) A hierarchical structure model of the causes of accidents according to ISM was built. The weight and proportion of accidents and deaths were combined to evaluate the importance of accident-causing attributes. Human factor-induced accidents accounted for 71.6 % of all accidents, and the number of accident death caused by organization management factors was the largest, accounting for 54.8 %. Comprehensive analysis revealed that human and organization management factors are the most important causes of coal mine accidents

LEGAL PROTECTION AND LIABILITY OF THE BUSINESS ACTORS AGAINST TOURISTS IN MT. AGUNG BALI

Incidents of accidents during risky climbs have been reported in Bali, and yet to date there are no specific legal regulations regarding mountaineering tourism in the region. Mountaineering tourism is a type of tourism with a high risk, making it very important to implement legal regulations that can provide protection to tourists in any undesirable situation. This research aims to evaluate the legal protection of tourists according to Law No. 10/2009 on Tourism as well as the liability of mountain climbing tourism business actors to those who have experienced losses. This is a qualitative research which adopted normative and conceptual approach. The research findings indicate that there is a necessity for additional regulations as stated in the Governor Regulation of Bali No. 52/2021 concerning Regulations for the Implementation of Regional Regulation No. 5/2020 concerning Standards for the Implementation of Balinese Cultural Tourism. In accordance with Article 1366 of the Civil Code, businesses or managers of the mountain climbing tourist attractions in Bali are obliged to be responsible in the case of accidents that have caused significant losses to tourists. The liability of business actors can also refer to Article 7 of Law No. 8/1999 on Consumer Protection. In addition, the liability of business actors as managers of tourist attractions is also referred to Article 26 of Law No. 10/2009 concerning Tourism and Article 6 No. 3 of Bali Province Regional Regulation No. 5/2020 concerning Standards for the Implementation of Balinese Cultural Tourism.

Safe Autonomy: Sensor Fusion in Complex Traffic Environments

Advancements in autopilot driving and car technology have propelled the development of safe autonomy, particularly in navigating complex traffic environments. This paper delves into applying sensor fusion techniques in automobile systems, focusing on their relevance in ensuring safe navigation amidst the intricacies of Indian road conditions. Of course, the conditions of Indian roads have changed hugely and drastically over time but still, it would not be wrong to say that India is still grappling with a high incidence of road accidents exacerbated by non-compliance with traffic rules, integrating sensor fusion technologies becomes pivotal in enhancing the safety and reliability of autonomous systems. By including and adding data from various sensors such as cameras, LiDAR, and radar, these systems gain a comprehensive understanding of their surroundings, enabling real-time decision-making in dynamic traffic scenarios. Furthermore, the paper explores the integration of machine learning algorithms to augment sensor fusion capabilities, facilitating adaptive responses to erratic traffic behaviour and irregular driving practices. Through a technology-centric lens, this research aims to play the role of sensor fusion in bolstering the efficacy of autonomous systems on Indian roads, thereby contributing to the mitigation of road accidents and promoting safer transportation infrastructures.

Data-driven automatic classification model for construction accident cases using natural language processing with hyperparameter tuning

The construction industry, while vital to societal progress, is marred by a high incidence of accidents and injuries. Manual classification of accident cases is intensive and susceptible to human bias. This study addresses this challenge by developing an automated accident case classification system for the construction industry using Natural Language Processing and machine learning techniques. This study was conducted using the following steps: (1) Establishment of dataset (2) Korean Natural Language Processing (3) Selection of machine learning models (4) Model evaluation. The models exhibited competitive performance, demonstrating high accuracy, precision, and recall rates across all classification tasks. XGBoost outperformed NB, SVM, and KNN for accident type, facility type, and work type with accuracy of 0.80, 0.56, and 0.67, respectively. The results also provided insights into the factors influencing accident classification. This study contributes to construction safety by providing a data-driven foundation for safety decision-making, resource allocation, and benchmarking.

Investigating Road Ice Formation Mechanisms Using Road Weather Information System (RWIS) Observations

Ice formation on roads leads to a higher incidence of accidents and increases winter de-icing/anti-icing costs. This study analyzed 3 years (2019–2021) of Road Weather Information System (RWIS) sub-hourly measurements collected by the Montana Department of Transportation (MDT) to understand the first-order factors of road ice formation and its mechanisms. First, road ice is formed only when the road pavement surface temperature is equal to or below the freezing point (i.e., 32 °F (i.e., 0 °C)), while the corresponding 2 m air temperature could be above 32 °F. Nevertheless, when the road pavement was below 32 °F ice often did not form on the roads. Therefore, one challenge is to know under what conditions road ice forms. Second, the pavement surface temperature is critical for road ice formation. The clear road (i.e., with no ice or snow) surface pavement temperature is generally warmer than the air temperature during both day and night. This feature is different from a natural land surface, where the land skin temperature is lower than the air temperature on cloud-free nights due to radiative cooling. Third, subsurface temperature, measured using a RWIS subsurface sensor below a road surface, did not vary as much as the pavement temperature and, thus, may not be a good index for road ice formation. Fourth, urban heat island effects lead to black ice formation more frequently than roads located in other regions. Fifth, evaporative cooling from the water surface near a road segment further reduces the outlying air temperature, a mechanism that increases heat loss for bridges or lake-side roads in addition to radiative cooling. Additionally, mechanical lifting via mountains and hills is also an efficient mechanism that makes the air condense and, consequently, form ice on the roads. Forecasting road ice formation is in high demand for road safety. These observed features may help to develop a road ice physical model consisting of functions of hyper-local weather conditions, local domain knowledge, the road texture, and geographical environment.

A decadal analysis of road traffic accident-related mortality among women of reproductive age: A cross-sectional study in East Azerbaijan Province, Iran.

Given the extensive impact of road traffic accidents (RTAs) consequences and their potential ramifications on the health of both current and future generations, this study examines the social and demographic factors that influence RTA-related mortality among women of reproductive age. The study population consisted of cases retrieved from the database of the Legal Medicine Organization, encompassing all women aged 15-49 who succumbed to road accidents between 2011 and 2021. The mean age of women in the reproductive age group from East Azerbaijan province between 2011 and 2021 was 33.67 years, with a standard deviation of 9.18. RTAs on main roads accounted for the majority of incidents (395 cases, 50.8%), with 93.7% (728 cases) attributed to road traffic. In 54.4% of these cases, the affected organ was the head and neck, and the primary cause of death in 52.1% was head trauma. Across all age groups, injuries to the neck and head were the most common, followed by injuries to the abdomen, chest, back, and sternum. The higher incidence of road accidents in the 25-29 age group highlights the need for targeted interventions to address risky behaviors, inexperience, and peer influences in this demographic. Our observation of passengers experiencing the highest mortality rate emphasizes the vulnerability of road users, particularly pedestrians, in traffic accidents. Pedestrian violations in the 17-30 age group further emphasize the importance of education and awareness campaigns aimed at reducing risky behavior.

Can Complexity add anything to Critical Realism and the Morphogenetic Approach?

AbstractComplexity is not ‘the same as simply complicated’. This is because its advocates present it as a theoretical approach to explaining major aspects of the social order, usually at the macro level, whereas many social phenomena, at any level, can be full of complications (such as the incidence of road accidents) without a unifying theoretical key. Thus, the latter have a strong tendency to remain at the level of events and their study to be confined to a ‘variables’ approach, statistically combining the most variable of potentially contributory factors, without being troubled by the absence, in particular cases of one or more common contributors to accidents (such as drivers’ alcohol consumption). But ‘the Complexity Turn’ does much more than leaving empiricism behind, like Critical Realism from its earliest beginnings, and in some hands is seen as the senior partner of these two approaches.

Fusion of satellite and street view data for urban traffic accident hotspot identification

As the number of vehicles and the volume of traffic swell in urban centers, cities have experienced a concomitant increase in traffic accidents. Proactively identifying accident-prone hotspots in urban environments holds the promise of preventing traffic mishaps, thereby curtailing the incidence of accidents and reducing property damage. This research introduces the Two-Branch Contextual Feature-Guided Converged Network (TCFGC-Net) utilizing multimodal satellite and street view data. Designed to extract global structural features from satellite imagery and dynamic continuous features from street view imagery, the model aims to improve the accuracy of detecting urban accident hotspots. For the satellite imagery branch, we propose the Contextual Feature Coupled Convolutional Neural Network (Trans-CFCCNN) designed to extract global spatial features and discern feature correlations across adjacent regions. For the street view imagery branch, we develop the Sequential Feature Recurrent Attention Network (SFRAN) to assimilate and integrate dynamic scene features captured from successive street view images. We designed the Multi-Branch Feature Adaptive Fusion Structure (MBFAF) to aggregate different branch features for accurate identification of accident hotspots. Experimental results show that the model performs well, with an overall accuracy of 93.7 %. Ablation studies confirm that relative to standalone street view and satellite branch analyses, implementing multimodal fusion enhances the model's accuracy by 12.05 % and 17.86 %, respectively. The innovative fusion structure proposed herein garners a 4.22 % increase in model accuracy, outpacing conventional feature concatenation techniques. Furthermore, the model outperforms existing deep learning models in terms of overall efficacy. Additionally, to showcase the efficacy of the proposed model structure, we utilize Class Activation Maps (CAM) to provide visual interpretability for the model. These results suggest that the dual-branch fusion model effectively decreases false alarm occurrences and directs the model's focus toward regions more pertinent to accident hotspots. Finally, the code and model used for identifying hotspots of urban traffic accidents in this study are available for access: https://github.com/gwt-ZJU/TCFGC-Net.

Temporal Characterization of Fire Accidents in China

Based on the fire accident statistics, this study uses the seasonal index method to comprehensively analyze the temporal pattern of fire occurrence in China from 2010 to 2015. The study scientifically reflects that the number of fire occurrences has an extremely close relationship with time, and summarizes the temporal pattern and characteristics of fire occurrences. This study concludes that the high incidence of large fire accidents in China occurs in the time period of 3:00 a.m. to 5:00 a.m. The probability distribution model of the average total number of accidents in each time period was established and the Kolmogorov test was carried out, which shows that the time characteristics of fire accidents in China can be understood with the probability distribution model as a whole. The results of the analysis of the time characteristics of the fire accidents can provide decision-making information for combating fire accidents.

Investigation of Car following and Lane Changing Behavior in Diverging Areas of Tunnel–Interchange Connecting Sections Based on Driving Simulation

Tunnel–interchange connecting sections pose significant safety challenges on mountainous expressways due to their high incidence of accidents. Improving road safety necessitates a comprehensive understanding of driver behavior in such areas. This study explores the influences of road characteristics, signage information volume, and traffic conditions on drivers’ car-following and lane-changing behavior in tunnel–interchange diverging areas. Utilizing driving data from 25 subjects of 72 simulated road models, driving performance is assessed using the Friedman rank test and multivariate variance analysis. The results highlight the significant influence of both connection distance and signage information load on driving behavior. In tunnel–interchange scenarios, the reduction in velocity increased by 62.61%, and speed variability surged by 61.11%, indicating potential adverse effects on driving stability due to the environmental transitions. Decreased connection distances are associated with reduced lane-changing durations, larger steering angles, and increased failure rates. Furthermore, every two units of increase in signage information leads to a 13.16% rise in maximum deceleration and a 5% increase in time headway. Notably, the signage information volume shows a significant interaction with connection distance (F > 1.60, p < 0.045) for most car-following indicators. Hence, the study recommends a maximum connection distance of 700 m and signage information not exceeding nine units for optimal safety and stability.

Construction safety management based on building information modeling technology

With the progression of society, urban construction has been rapidly advancing, yet the incidence of construction safety accidents has surged. In order to effectively prevent construction safety incidents, construction safety management has become increasingly significant. To achieve the safety management of construction projects, Building Information Modeling (BIM) technology is introduced. It can help construction safety management departments better understand every link in the entire construction process. In this paper, an integrated analysis of BIM technology and the current state of construction safety is presented, aiming to explore the problems existing in the current construction safety management and provide construction personnel and management staff with a comprehensive understanding of predictable risk factors, effective preemptive measures, safety inspections, identification of safety hazards, and other associated issues, thereby guaranteeing the occupational safety and security of construction personnel and property. Consequently, the accident prevention model undergoes a transformation from a reactive approach to a proactive one.

RT Based Railway Gates Automation Using NI myRIO

Railway safety at level crossings remains a paramount concern due to the recurring incidence of train accidents. This study proposes an innovative solution to enhance safety by introducing an automatic gate system at unmanned crossings, replacing manual operation. The system utilizes strategically positioned proximity detectors to detect approaching trains, automating gate management. This reduces gate closure duration compared to manual operation, minimizing disruptions to road traffic. Additionally, the system reduces reliance on human labor, offering a more efficient and reliable solution. It is particularly suitable for unmanned crossings prone to higher accident probabilities, where dependable operation is crucial. By integrating infrared sensors and servo motors, the system ensures prompt gate closure upon train detection, reducing potential accidents caused by negligence. This study enhances prior research by utilizing two infrared sensors on each side of the track, improving train position tracking accuracy. The sensors are programmed using the LabVIEW platform, facilitating integration with myRIO controllers. Overall, this study presents a comprehensive approach to enhancing railway safety through innovative automation technology at unmanned level crossings. Keywords— NI myRIO, IR sensors, Servomotor, Labview.

Work-Related Injuries Among Epileptic Employees: A Scoping Review

Background: Epilepsy is a non-communicable common neurological defect that can affect individuals of all ages. Working is essential for epileptic individuals; however, it can also be dangerous if proper measures are not put in place to minimize the risks of injury. It helps individuals become independent and contribute to society and can assist in improving the lives and self-worth of epileptic individuals. The study aims to know the linkage between epilepsy and injury at the workplace and its risk factors if it influences epileptic patients' lives. Moreover, types of epilepsy have the majority causing injury among employees. Our objective is to identify the relationship between epilepsy and injuries at the workplace, investigate the quality of life among them to ascertain the impact and risk factors that epilepsy caused, and identify which type of seizure is at higher risk of injury among employees. Method: According to the Arksey and O’Malley framework, we searched PubMed, Summon, and Ovid databases for English full-text articles published between 2005 and 2022 using search terms’ epilepsy' and ‘injury’ and ‘employee’ in the title. Results: Our study contains eight articles included in our review as they meet the criteria; four focus on epilepsy and injury, and the other focus on epilepsy and the workplace. Conclusion: People with epilepsy (PWE) has a higher unemployment rate because of the incidence of accidents at the workplace. Fear of having seizures during work time which is a significant effect on epileptic individuals' work state. Employers and Co-employers at the workplace have a negative attitude toward epilepsy, which further isolates PWE. Some injuries and ignorance to deal with it can prevent the patient from having a normal life.

Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change

Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perform safe and appropriate lane change is a popular topic of research in the field of autonomous driving. Currently, few papers consider the safety of reinforcement learning in discretionary lane-change scenarios. We introduce safe hybrid-action reinforcement learning into discretionary lane change for the first time and propose the Parameterized Soft Actor–Critic with PID Lagrangian (PASAC-PIDLag) algorithm. Furthermore, we conduct a comparative analysis with Parameterized Soft Actor–Critic (PASAC), which is an unsafe version of PASAC-PIDLag. Both algorithms are employed to train the lane-change strategy to output both discrete lane-change decisions and continuous longitudinal vehicle acceleration. Our simulation results indicate that at a traffic density of 15 vehicles per kilometer (15 veh/km), the PASAC-PIDLag algorithm exhibits superior safety with a collision rate of 0%, outperforming the PASAC algorithm, which has a collision rate of 1%. The generalization assessments reveal that at low traffic density levels, both the PASAC-PIDLag and PASAC algorithms are proficient in attaining zero collision rates. However, at high traffic density levels, although both algorithms result in collisions, PASAC-PIDLag has a much lower collision rate than PASAC.

What Leads to Effective Emergency Management? A Configurational Analysis of Empirical Cases of Local Chinese Governments

This study investigates the foundational elements that contribute to effective emergency management in urban settings, with a particular focus on experiences from Chinese municipalities. Drawing on resource dependence theory and attention allocation theory, this research develops an analytical framework encompassing four pivotal factors: environmental resources, information sharing, social participation, and leadership attention allocation. Utilizing Qualitative Comparative Analysis (QCA), we examine these factors across sixteen critical incidents of urban accidents and disaster responses in China. Our findings reveal that a high degree of leadership attention allocation is an essential prerequisite for municipal governments to exhibit robust emergency management capabilities during crises. Furthermore, two primary pathways affecting emergency management capacity were identified: the “resource–leadership attention type” and the “social participation–resource coordination type”. These findings contribute to a nuanced understanding of the complexities of emergency management and enlighten the local governments to take some effective measures to enhance emergency management capacities to mitigate disaster losses.

Safe beach: service for emergency notification and assistance in coastal bathing areas

On the vast Brazilian beaches, which stretch for more than seven thousand kilometers and attract numerous tourists, safety emerges as a crucial concern. The shortage of lifeguards and the lack of human resources have generated concern due to the incidence of fatal accidents in bathing areas with low protection coverage. To reinforce security logistics on the coast, the “Safe Beach” service was created, which allows swimmers to report emergencies and call lifeguards based on their location through an application for mobile devices. This article explores the functionality of the service, highlighting its potential for reducing the response time of lifeguard teams, preventing incidents in risk areas and improving safety on beaches in Brazil. It also addresses the importance of responsible use of the service and the legal implications for those who use it improperly. The combination of advanced technology, usage records and cooperation with local authorities makes Safe Beach a powerful tool for ensuring safety on beaches and agile response in emergency situations, contributing to a safer and more peaceful experience for bathers.

Android Accident Detection and Alert System

Increasing concern for road safety has led to the search for new technological methods to reduce the incidence of traffic accidents. This article describes the design, development, and evaluation of an Android-based incident detection and notifi- cation system that aims to improve emergency response capabili- ties and reduce incident severity. The system leverages ubiquitous smartphones and their built-in sensors by combining accelerom- eters, GPS, and image processing technology to instantly capture and share events. When detected, the system triggers an alarm to notify emergency services and first responders of the incident’s location and important information. The system also has features such as automatic emergency call initiation to provide immediate assistance to victims. The effectiveness of the system has been evaluated through various tests conducted in various simulated accident situations, demonstrating its accuracy, reliability and ability to shorten time in emergency situations. This research contributes to the advancement of smart transportation by using mobile technology to improve road safety and reduce the risk of accidents. Index Terms—Android-based system, accident detection, alert- ing, real-time monitoring, integrated sensor, accelerometer, GPS, imaging, emergency response, machine learning, automatic emer- gency calls, security, smart transportation, ubiquitous computing, smart mobile applications.