Accident Record Research Articles

The Effect of Suspected Alcohol Intoxication on Fatality and Injuries of Road Users in Metro Manila — 2005-2020

Introduction. The ever-increasing global road traffic accidents is caused by several risk factors: human factors, vehicle factors, and road environmental factors. In the Philippines, 12,000 road users die annually due to road crashes and almost one-third are alcohol-related crashes.Objectives. This study aims to investigate the effects of suspected alcohol intoxication on the severity of injuries among drivers and passengers in a major urban region in the Philippines using the Metro Manila Accident Recording and Analysis System (MMARAS) of the Metropolitan Manila Development Authority from 2005 to 2020.Methods. The Metro Manila Accident Recording and Analysis System (MMARAS) was used to determine the data on trends and association for alcohol-related road crashes, while descriptive and inferential statistics such as logistic and multinomial regression were used to determine trends and associations.Results. Suspected alcohol intoxication road crash cases in Metro Manila have decreased from 45 cases in 2005 to 31 cases in 2020. The year 2011 recorded the highest cases of alcohol-suspected road crashes. The years 2010, 2011, and 2012 recorded the highest number of driver’s death while 2005, 2006, 2014, and 2019 recorded higher deaths for passengers. Cars and motorcycles are the most commonly involved vehicles in alcohol-suspected road crashes, while Quezon City (n=307) and Marikina City (n=267) are the common places of incidence for drunk-driving accidents. On the other hand, alcohol intoxication increases the risk of driver’s death (OR=9.16; 95% CI [5.388-15.574]) and injury (OR=5.22; 95% CI [4.487-6.074]). While intoxicated, hitting an object (OR = 12.557; 95% CI [1.478-106.694]) and truck collisions (OR=7.176; 96% CI [1.261-40.817]) can increase the risk for driver’s death. Meanwhile, side swipe (OR=2.330; 95% CI [1.029-5.277]) and angle impact (OR= 7.972; 95% CI [1.804-35.227]), increase the odds for driver injuries.Conclusion. Suspected alcohol intoxication road crashes can be prevented; however, a collaborative effort is needed. The government is responsible for road safety education as well as effectively enforcing road safety-related laws, while the road users shall use the roadways responsibly.

Assessment on safety practices in retrofitting of schools project of Kathmandu district

Construction safety is a challenging issue in construction industry all around the world. For the construction works, regular monitoring and supervision are the important parameters. After the massive Gorkha Earthquake of April 25th, 2015 (Nepal), large number of schools have been affected hence to enhance repair, maintenance, rehabilitation and improve of this situation a large number of retrofitting projects have been added to the list of construction projects of Nepal especially school buildings .With the increase in construction projects, a large number of accidents have also been increased tremendously. Safety legislation for construction industry in developing countries is not sufficient in order to make construction a safe industry as well as prevailing rules and regulations were not properly implemented at construction sites because of poor civic awareness, knowledge regarding safety, training, and workshop. It was found that training, education and experience of workers had a slightly minor role in decreasing the possibility of occurrence of accidents at site. Management shall also focus on accident record keeping and safety meeting with safety audit as no such records was maintained at the sites visited.

On Appropriate Evaluation Methodologies in the Context of Using Both Accident and Health Record Data.

Accident and emergency informatics has become a new approach to accident research in the era of digitization, where it has become realistic to integrate data recorded at accident sites with data in electronic health records of patients. This chapter deals with the question on whether the existing and well-established evaluation methodologies used in accident-centered research as well as in patient-centered research within clinical medicine are sufficient and should also be used for such integrated data or whether they have to be modified or extended. Based on the Gaus-Muche-Nomenclature on studies in clinical medicine, it will be outlined which types of studies are appropriate here. In addition to observational studies and registers, controlled trials using randomization are also be regarded as an important approach for gaining new knowledge. In order to appropriately access data from health records and from accidents, standards for representing and communicating data for such studies will be of importance. Another criterion is referential integrity. Here and with respect to accidents the International Standard Accident Number (ISAN) may be of importance.

Relational Graph Convolutional Network for Text-Mining-Based Accident Causal Classification

Accident investigation reports are text documents that systematically review and analyze the cause and process of accidents after accidents have occurred and have been widely used in the fields such as transportation, construction and aerospace. With the aid of accident investigation reports, the cause of the accident can be clearly identified, which provides an important basis for accident prevention and reliability assessment. However, since accident record reports are mostly composed of unstructured data such as text, the analysis of accident causes inevitably relies on a lot of expert experience and statistical analyses also require a lot of manual classification. Although, in recent years, with the development of natural language processing technology, there have been many efforts to automatically analyze and classify text. However, the existing methods either rely on large corpus and data preprocessing methods, which are cumbersome, or extract text information based on bidirectional encoder representation from transformers (BERT), but the computational cost is extremely high. These shortcomings make it still a great challenge to automatically analyze accident investigation reports and extract the information therein. To address the aforementioned problems, this study proposes a text-mining-based accident causal classification method based on a relational graph convolutional network (R-GCN) and pre-trained BERT. On the one hand, the proposed method avoids preprocessing such as stop word removal and word segmentation, which not only preserves the information of accident investigation reports to the greatest extent, but also avoids tedious operations. On the other hand, with the help of R-GCN to process the semantic features obtained by BERT representation, the dependence of BERT retraining on computing resources can be avoided.

Control of Occupational Safety and Health Risk Using the Hirarc Method (Case Study : PT Sari Warna Asli V Kudus)

PT Sari Warna Asli V is a yarn spinning manufacturing company. Based on the company's work accident record, in 2019 in the production department there were 19 work accidents and in 2020 as many as 18 work accidents. Total work accidents in 2019 were 14 cases that occurred in Spinning Mills I and 5 cases in Spinning Mills II. Meanwhile, in 2020, 12 cases occurred in Spinning Mills I and 6 cases in Spinning Mills II. The purpose of this research is to identify the potential hazards that exist, assess the risks that may occur at each work station in Spinning Mills I, so that risk control can be given so that the company can avoid any possible harm. The method used is Hazard Identification Risk Assessment Risk Control (HIRARC). Research data collection was obtained from observations at Spinning Mills I, interviews, documentation, and literature studies. The results of the study at 6 work stations on repair and maintenance activities obtained the percentage of potential hazards of 61% (49 risks) are low risk (low risk), 13% (10 risks) are moderate risk (moderate risk), 26% (21 risks) are high risk (high risk), and there are not potential work hazards with very high category (extreme risk). Risks included in the high risk category are falling from above when clearing blockages, broken hands/fingers exposed to rotating machines, fire, hearing loss, and fatigue. Risk control that can be done by the company includes adding SOPs, safety signs, and safety talk, K3 training to increase worker knowledge, providing PPE that must be used according to standards, installing automatic sensors that can detect approaching hands, providing safe distance limits/floor markings. production and routine cleaning.

Assessing the validity of navigation risk assessments: A study of offshore wind farms in the UK

The developments of offshore wind farms can place increased pressures on conflicting marine users, particularly in already crowded waterways. Risk analysis of potential hazard scenarios are conducted by developers and regulators in the form of Navigation Risk Assessments which seek to identify, measure and mitigate impacts through data collection, consultation, modelling and risk assessment. These activities have inherent uncertainties and limitations which are rarely discussed and have the potential to undermine the value and credibility of the risk assessment. To evaluate the accuracy of Navigation Risk Assessments, their predictions are compared with the historical incident record of accidents involving wind farms. This review identifies significant methodological limitations and sources of uncertainty endemic in the Navigation Risk Assessment process which results in an over-estimation of risk. These include a lack of inclusion of historical evidence, issues during elicitation of expert judgment and methodological limitations of both quantitative risk models and the underlying risk assessment. Based on our evaluation, future research directions are highlighted to support decision makers on marine spatial planning by increasing the robustness of Navigation Risk Assessments.

Cause analysis of hot work accidents based on text mining and deep learning

Hot work accidents have significant consequences. Admittedly, preventing hot work accidents requires managers to analyze the accident profoundly and learn from the requisite documents that contain the detailed process and causes of the accident. However, the analysis process of unstructured records is manual, leading managers to failing to quickly analyze the cause of the accident and identify the key causes. Therefore, we used deep learning to automatically classify and predict the cause of accidents by the textual features and text mining methods that quickly extract the cause information to optimize accident record analysis. Initially, the latent Dirichlet allocation model was adopted to extract the topic words in the accidents to form cause topics, and convolutional neural networks were trained to predict the cause of the accident based on the previous cause topics. Then, the key causes of hot work accidents were extracted by qualifying the importance score of the cause, which included no gas detection and continuous monitoring, uncleaned combustibles, improper protection measures, and violations of regulations by workers. Managers can utilize these key causes to formulate optimal safety strategies to reduce the number of accidents. This study provides valuable suggestions for improving the process safety management of hot work in China. Moreover, the results of our method can be used as important documents for the safety education and training of the enterprise.

Incorporating multi-scenario underreporting rates into MICE for underreported maritime accident record analysis

This study attempts to incorporate multi-scenario underreporting rates into multivariate imputation using chain equation (MICE) to address underreporting issues in maritime accident records. In addition, the Tobit regression model is utilized to evaluate the economic loss of maritime accidents for both the original and supplemented data. With ten years’ maritime accident data in Fujian waters, one empirical study is created to testify the effectiveness of the proposed method. The marginal effect results from the proposed method significantly differ from the conventional Tobit regression model. The proposed method could significantly correct the over- or underestimated impacts related to various variables (e.g., serious/non-serious accident types, fishing vessels, other ships, and liquid cargo ships). This reveals that the proposed method is capable of correcting the systematic deviation of the analysis results to a satisfactory extent.

Research on improved full-factor deep information mining algorithm

In the use of fire-fighting physics platform for fire alarm data correlation analysis, there are often problems such as too much data volume and insufficient accuracy of the analysis results. For such questions, this paper establishes a full-factor secondary mining mechanism for fire accidents based on the fire big data based on the correlation analysis algorithm and the clustering algorithm. The association algorithm is used to conduct full-factor primary mining on the fire-related factors in the data warehouse, and the common-sense accident attributes in the association rules are extracted. Then use the K-means clustering algorithm, where the cluster center is the relevant attribute in the fire accident record, and perform the second combined clustering of the accident elements to achieve in-depth information mining of all factors of the fire accident. Experimental results show that the improved full-factor deep information mining algorithm proposed in this paper can effectively filter 31.6% of meaningless mining results compared to the traditional single mining algorithm. It shows that the algorithm in this paper can more accurately dig out the relationship between data, and can provide more effective decision support for fire management and other work.

Traffic safety culture of drivers in Canada: Implications for new traffic law implementation to enhance traffic safety

This study investigates Canada's traffic safety culture (TSC) as part of a global research project. The TSC survey data collected by an online survey is used to predict drivers' perception of changes in traffic problems in the past 3 years, driver's support, opposition towards enforcement of additional traffic laws, and drivers' perceived threat towards risky driving behaviors. A two-step procedure is followed to build models. The first step includes feature selection using the chi-square test of independence. The second step comprises building classification models using the Random Forest technique. Results suggest that drivers' personal attributes like the number of accident records, driving frequency, geographic region of nationality, and religion are top predictor variables for drivers' perception towards changes in traffic problems. In addition, compared to others most drivers perceive distracted driving as a major traffic problem today. There is strong disapproval of drivers against the following driving behaviors and strong support to implement laws against it: speeding in school zones, talking on a hand-held cell phone, tying text messages or e-mails, drowsy driving, driving without wearing their seatbelt, drive with passengers not wearing seatbelts, running through red lights, and impaired driving. In contrast, following risky driving behaviors is less of a perceived threat: speeding over limit on a freeway, on a residential street, and in an urban area and talking on a hands-free cell phone while driving. In addition, a driver's accident record is a significant indicator for a perceived threat towards risky driving behaviors followed by age and accumulated demerit points. The results from this study can be used to guide educational campaigns to transform the traffic safety culture of target groups and make more informed policy decisions.

Spatial Distribution of Road Traffic Accident at Hawassa City Administration, Ethiopia

Globally, road traffic accidents (RTAs) are the leading killer of young people and are projected to be the 7th leading cause of death by 2030. This study is aimed at analyzing the spatial distribution of road traffic accident and identifying hotspot areas across Kebeles (smallest administrative division in Ethiopia) of Hawassa city administration in Ethiopia. Secondary data on daily traffic accident record from October 2013 to June 2018 was obtained from Hawassa city administration police department. The spatial clustering and hotspots identification were carried through Moran's I and Getis-Ord Gi* statistics. Data analysis was conducted using GeoDa 1.16.0.0 and ArcGIS 10.2 softwares. Drivers within age group of 18-30 years, who were hired by private business owners and who had no driving license committed the highest number of traffic accidents. The majority of traffic accidents were caused due to careless driving, failure to give priority for pedestrian, high speed and driver failure to give priority for each other. In addition, about 82.01% of traffic accidents were recorded on asphalts road and 11.51% by gravel road. Spatial clustering of road traffic accidents for accidents occurred on gravel road and in sunny weather conditions found to be significant. Different hotspot areas were identified for gravel type of road and sunny weather condition. The concerned government bodies involved in policymaking are recommended to give special attention to young driver who were hired by private business owners. Interventions to mitigate the occurrence of traffic accident would take in to account the identified hotspot areas.

Analysis of the causes and effects of cyclist-pedestrian accidents in biggest Polish cities

Traffic incidents between pedestrians and cyclists result in an incomparably smaller number of victims (injured and killed) than accidents between unprotected traffic participants and other vehicles. However, such incidents cannot be underestimated, as in most cases they take place on elements of infrastructure designed for pedestrians and cyclists, and thus negatively affect the sense of safety of users in places where they should not only feel safe but also comfortable. This paper presents an analysis of such traffic incidents, aimed at recognizing the share of pedestrians and cyclists as perpetrators and also victims of accidents. Three research hypotheses were examined: that the type of infrastructure and also light and weather conditions influences the structure of perpetrators and victims, that the proportion of accidents between pedestrians and cyclists caused by pedestrians is increasing, and that the proportion of victims of accidents between pedestrians and cyclists in the pedestrian group is de-creasing. Analyses were performed based on the number of traffic incidents involving cyclists and pedestrians in the six largest Polish cities, registered in the Police Accident and Collision Records System. A total of 1,702 incidents involving 1,034 injured and killed people in years 2007-2018 were considered. Each traffic incident was considered individually, taking into account changes in perpetrator and victim proportions depending on the type and purpose of the infrastructure and external conditions, as well as variability of these proportions over subsequent years. The tools of mathematical statistics were used, including – among others - significance tests for independent proportions and Chi-squared test for trend. On the basis of the conducted statistical analyses, all research hypothesis were proved. It also confirmed that although the proportions are changing, there are still much more traffic incidents are caused by cyclists, but more victims are in group of pedestrians. The results of the research confirm the need to take action to develop effective mechanisms of mutual interaction between pedestri-ans and cyclists. Especially in view of the growing bicycle traffic in Polish cities.

Applications of Data Mining in Mitigating Fire Accidents Based on Association Rules

<p class="0abstract">Due to the increased rate of fire accidents which cause many damages and losses to people souls, material, and property in Basra city. The necessity of analyzing and mining the data of the fire accidents became an urgent need to find a solution. The need increased for a solution that helps to mitigate and reduce the number of accidents. In this paper, data mining techniques and applications including data preprocessing, data cleaning, and data exploration have been applied. Data mining applications is performed to analyze and discover the hidden knowledge in ten years of data (fire accidents happened from 2010 – 2019) which is approximately 20k record of accidents. These data mining techniques along with the association rules algorithm is applied on the dataset. The applied approach and techniques resulted in discovering the patterns and the nature of the fire accidents in Basra city. It also helped to reach to recommendations and resolutions for mitigating the fire accidents and its occurrence rate.</p>

An evaluation of highway crash-prone areas: A case study on Pan Borneo Highway in the state of Sarawak

Road traffic crashes is one of the major causes of death that needs to be addressed globally. Many studies have been conducted to identify the contributing factors to traffic crashes and to determine the required preventive measures. This study evaluates the causes of 164 traffic crashes along a 10 kilometres section of Pan Borneo Highway Sarawak. The study investigated the main causes of traffic crashes within this section of road through multiple sources: based on expert on site evaluation, analysis of site incident report and police accident record. The finding reveals that most of the accidents (93%) occurs during the daytime, weather contributes a combined 69% and private car owners (80%) cause most of the traffic crashes. The study also indicates that the road condition is a significant factor to the occurrence of traffic crashes in that area contributing 64% of the total crashes.

Incorporating accident liability into crash risk analysis: A multidimensional risk source approach

In the field of traffic safety, the occurrence of accidents remains a cause of concern for road regulators as well as users. Exploring risk factors inducing the accidents and quantifying the accident risk will not only benefit the prevention and control of traffic accidents but also assist in developing effective risk propagation model for road accidents. This study uses detailed accident record data to mine the risk factors affecting the occurrence of accidents, and quantify the accident risk under the combination of risk factors. First, by reviewing relevant literature and analyzing historical accident, we construct a multi-dimension characterization framework of risk factors with bi-level structure. The Human Factors Analysis and Classification System (HFACS) is applied to supplement and improve the framework. Next, under this framework, we identify the risk factors in traffic accident record, and analyze the statistical characteristics from the level of risk sources and risk characteristics. Then, the concept of accident liability weight is proposed to measure the impact of risk factors on accident occurrence. Through the liability affirmation of risk factors, the accident probability are updated. Last, we establish an accident risk quantify model (ARQM) based on the mean mutual information to compare the likelihood of accidents in different scenarios. In addition, we compare the accident probability and risk under equivalent liability and liability affirmation, as well as give some fundamental ideas regarding how to effectively prevent accidents.

Avaliação epidemiológica de acidentes com animais peçonhentos no Oeste da Bahia

Introdução: No Brasil é crescente o número de acidentes por animais peçonhentos e a principal causa desse cenário é o desequilí­brio ecológico ocasionado geralmente por ações humanas, o que configura um preocupante quadro de saúde pública e epidemiológico. Objetivo: Apresentar quantitativamente os í­ndices de acidentes ocasionados por animais peçonhentos registrados nas localidades do municí­pio de Barreiras - BA. Método: O presente artigo é uma pesquisa de campo de caráter quali-quantitativo. Resultado: A Zona Rural apresentou maior í­ndice de notificações, somando 58 notificações nos dois anos, 34 em 2018 e 24 em 2019, dentre os animais, o escorpião é o que apresenta o maior registro de acidentes. Conclusão: Os í­ndices de acidentes por animais peçonhentos nas cidades ainda configuram um preocupante cenário, apresentando risco í saúde da população da região. Esses acidentes continuam a constituir um sério problema de saúde pública no Brasil.

Routing for safer rides: a space syntax approach to predict bicycle collisions in a Brazilian city

Abstract In order to broaden the discussion on the safety of bicycle transport, this paper uses the analytical capacity of the spatial syntax applied to the recording of accidents involving cyclists in the city of Rolândia-PR, located in the Southern Region of Brazil. With the availability of 535 reports of trips made by bicycle mode, collected in the survey origin and destination of the city, a database was elaborated where each segment of the road received its numerical value from the loading of trips, and its corresponding values of choice and integration, generated in the Depthmap software. As a result, the relationship between the point of accident and the trip record by bicycle was refuted. In contrast, the angular values of choice and integration were sufficient to explain the occurrence of accidents involving cyclists in each segment of the municipal urban network, statistically proven through the generation of a generalized linear model. The contribution of this study focuses on the validity of using spatial syntax to predict safer routes, which is considered a theoretical-methodological approach that identifies priority routes for the implementation of specific infrastructure for bicycle transport.

Fatal Crashes Among Drivers Age 60+ in Connecticut, Massachusetts, New Hampshire, and Rhode Island

Fatal crashes are related with the spatial components of physical environments (e.g. roadways, land use, and political boundaries). This study compares rates of fatal crashes among drivers age 60+ by counties in 4 states of CT, MA, NH, and RI. The GIS application is used to visualize the location of fatal crashes and to identify whether it is clustered as hotspots. This study pooled data related to fatal crashes in CT, MA, NH, and RI from the Fatal Accident Recording System (2008-2018). Sample (n=2,373) inclusion criteria were subjects (driver age 60+) had to have complete data on variables of interest and be involved in a crash with at least one fatality. More than half (n=1,387, 58.5%) of drivers had a fatal injury. Results showed that the county with the highest incidents of fatal crashes was New Haven, CT (n=183 involved, 53% fatality), Worcester, MA (n=179, 61.5% respectively), Hillsborough, NH (n=75, 65.3% respectively), and Providence, RI (n=94, 59.6% respectively). The GIS spatial analysis showed that crashes were clustered along roads with the highest speed limits (interstate highways or multilane state routes) and found that the hotspots of clustered fatal crashes were located in counties with big cities with high population densities (New Haven CT, Hartford CT, Springfield MA, Worcester MA, Boston MA, Concord NH, and Providence RI). Identification of these crash hotspots will be beneficial for drivers and policy makers. The findings may alert drivers to high risk areas and policy makers can implement countermeasures.

MANAGEMENT COMMITMENT AND SAFETY TRAINING AS ANTECEDENT OF WORKERS SAFETY BEHAVIOR

The manufacturing sector in Malaysia is categorized as one of the dangerous work sectors with a high accident record. The use of heavy machinery, unsafe working conditions, and hazardous materials handling is among the causes of many work-related accidents recorded among factory workers. In addition, the lack of safety training and low safety management commitment towards worker safety also contributed to the increasing number in this statistic. The goal of this investigation is to determine the link between management commitment and safety training on safety behaviour among workers. This study involved small and medium factory workers operating in the northern states of peninsular Malaysia. The data for this study was obtained using a questionnaire, while the Statistical Packages for Social Science (SPSS) application was used to perform the data analysis process. The results of the data analysis show that employers' commitment to safety and safety training has a positive relationship with safety behaviour among study respondents. Suggestions and improvements to the study are presented at the end of this writing.

A Comparison of Motorcycle Helmet Wearing Intention and Behavior between Urban and Rural Areas

The motorcycle is one of the important modes of transport for Thai people in all provinces due to its convenience and ability to access all areas and cover short distances, which is especially convenient for rural people. However, according to the accident record, it was found that the motorcycle was the vehicle causing the highest amount of accidents, and helmet wearing could save lives and reduce the level of severe injuries. In this regard, the objective of this study was to study and develop a model of factors that affected helmet use behavior using structural equation modeling (SEM) based on the Health Belief Model (HBM). Further, this study compared urban and rural models, so as to suggest suitable guidelines for the promotion of helmet use in the study areas. The sample comprised 801 motorcycle users divided into 401 urban residents and 400 rural residents. From the parameter invariance testing in the two areas, a chi-square difference test found differences in the factor loading, intercepts, and structural paths between urban and rural societies.