Prevention Of Road Traffic Accidents Research Articles

Knowledge on Road Traffic Accidents among Taxi Drivers in Kathmandu Valley

Background: Kathmandu Valley experiences over 5,500 road traffic accidents annually, causing significant fatalities. Despite taxis being a safer transport option, they are still involved in accidents that is attributed to compromised knowledge related to road traffic accidents (RTAs). This study investigates taxi drivers' knowledge on road traffic accident prevention in Kathmandu Valley.Methods: The study used a cross-sectional design and was conducted across various junctions in Kathmandu Valley. A sample of 322 taxi drivers was selected using a purposive method. Data were collected through semi-structured interviews, with pre-testing conducted beforehand. Data were entered into Epi- Data and analyzed using SPSS version 16, with all ethical considerations strictly adhered. Results: The study found that 98% of the drivers were male, with a mean age of 37 years. Most were married, and over half identified as Janajatis. About half had secondary education, and 80% earned less than 50,000 per month. Despite 86% being satisfied with their income, knowledge gaps were evident. On average, 42% had poor knowledge of human-related factors, 62% lacked understanding of vehicular factors, and 20% were incorrect about environmental factors affecting RTAs.Conclusions: The study highlights significant gaps in taxi drivers' knowledge about factors contributing to RTAs. The findings suggest that targeted education programs and stricter regulations could help improve drivers' understanding and potentially reduce RTAs in the future.

The role of social workers in road traffic accident prevention communication for secondary school students in Danang City

Based on the analysis of the current status of road traffic accidents in Danang city and secondary schools in the area, the author analyzes the role of social workers in road traffic accident prevention communication for secondary school students. Most secondary school students are aware of the role of social workers in communication to prevent road traffic accidents and have made suggestions to improve the effectiveness of social work communication in preventing road traffic accidents for secondary school students in the study area.

Ефективність заходів щодо запобігання дорожньо-транспортним пригодам: огляд сучасних стратегій та підходів

This article investigates the problem of road traffic accidents (RTA) and reviews current strategies and approaches to prevent and reduce them. The significance of this problem is that the health of road users and the safety of vehicle traffic on roads is one of the most important global societal issues, especially with the continuous growth in the number of vehicles and the pace of urban activity. The article examines various aspects of road traffic accident prevention, including legal, technical, psychological and educational measures. It analyzes current trends in the development and implementation of road safety programs, as well as successful practices to reduce the number of accidents and injuries on the roads. Based on a study of the effectiveness of various strategies and programs aimed at reducing the number of accidents and casualties in traffic accidents, the article provides information on which measures are most effective and how they can be implemented in different contexts. In addition, it emphasizes the importance of cooperation between the authorities, public organizations and citizens in implementing road safety. The article also discusses in detail the importance of preventive measures, which may include educational campaigns and awareness-raising activities on vehicle safety on the roads. It also analyzes the legal norms and penalties for violations of traffic rules and the possibility of studying and using the experience of other countries, in particular Poland, in ensuring the safety of vehicles on the roads. The concluding part of the article discusses the prospects for the future development of road safety systems and the challenges that may arise in the future. The findings of this study can be useful for policy makers, researchers, and road safety professionals to improve road safety and reduce the number of road traffic accidents.

How to prevent road traffic accidents leading to passenger vehicle drowning

How to prevent road traffic accidents leading to passenger vehicle drowning

Ensemble Learning-based Algorithms for Traffic Flow Prediction in Smart Traffic Systems

Due to the tremendous growth of road traffic accidents, Intelligent Transportation Systems (ITSs) are becoming even more important. To prevent road traffic accidents in the long term, it is necessary to find new vehicle flow management techniques in order to optimize traffic flow. With the high growth of deep learning and machine learning, these methods are increasingly being used in ITSs. This research provides a novel conceptual ITS model that aims to predict vehicle movement through the collective learning usage to anticipate intersections. The proposed approach consists of three main stages: data collection through cameras and sensors, implementation of machine learning and deep learning algorithms, and result evaluation, utilizing the coefficient of determination (R-squared), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To accomplish this, various machine learning and deep learning algorithms, such as Random Forest, LSTM, Linear Regression, and ensemble methods (bagging), were incorporated into the model. The findings revealed the enhancement due to the proposed method, which was observed through a significant performance improvement of 93.52%.

Road traffic accidents in South Africa: challenges and solutions

Road traffic accidents are a major, but neglected global public health problem, requiring intensive efforts for effective and sustainable prevention. Of all the systems that people must deal with daily, road transport is the most complex, and the most dangerous. Road safety is an issue of national concern, considering its magnitude, gravity and the consequent negative impacts on the economy, public health and the general welfare of the people in South Africa. Road traffic accidents are a collective responsibility and all major stakeholders need to play their role in road safety and ensure that road users are safe whenever they are on the road. One of the major challenges for road traffic authorities in preventing road traffic accidents in South Africa is the inability of road users to understand and interpret the rules of the road and this contributes to a high rate of fatalities on the road. This qualitative article adopted the non-empirical research design: Systematic review. This study is completely based on the secondary data. A systematic review was done in detail for the collected literature. The key findings indicated that road users do not obey the rules of the road: jaywalking, speeding, driving and walking while impaired contribute to a high rate of fatalities on the road. The findings further indicated that law enforcement officials are not enforcing the law consistently which compromises the road safety project. Based on the findings, the author provided possible recommendations such as providing road safety campaigns throughout the year; capacitating law enforcement officials with resources to prevent road traffic accidents.

Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

Against the backdrop of rising road traffic accident rates, measures to prevent road traffic accidents have always been a pressing issue in Taiwan. Road traffic accidents are mostly caused by speeding and roadway obstacles, especially in the form of rockfalls, potholes, and car crashes (involving damaged cars and overturned cars). To address this, it was necessary to design a real-time detection system that could detect speed limit signs, rockfalls, potholes, and car crashes, which would alert drivers to make timely decisions in the event of an emergency, thereby preventing secondary car crashes. This system would also be useful for alerting the relevant authorities, enabling a rapid response to the situation. In this study, a hierarchical deep-learning-based object detection model is proposed based on You Only Look Once v7 (YOLOv7) and mask region-based convolutional neural network (Mask R-CNN) algorithms. In the first level, YOLOv7 identifies speed limit signs and rockfalls, potholes, and car crashes. In the second level, Mask R-CNN subdivides the speed limit signs into nine categories (30, 40, 50, 60, 70, 80, 90, 100, and 110 km/h). The images used in this study consisted of screen captures of dashcam footage as well as images obtained from the Tsinghua-Tencent 100K dataset, Google Street View, and Google Images searches. During model training, we employed Gaussian noise and image rotation to simulate poor weather conditions as well as obscured, slanted, or twisted objects. Canny edge detection was used to enhance the contours of the detected objects and accentuate their features. The combined use of these image-processing techniques effectively increased the quantity and variety of images in the training set. During model testing, we evaluated the model’s performance based on its mean average precision (mAP). The experimental results showed that the mAP of our proposed model was 8.6 percentage points higher than that of the YOLOv7 model—a significant improvement in the overall accuracy of the model. In addition, we tested the model using videos showing different scenarios that had not been used in the training process, finding the model to have a rapid response time and a lower overall mean error rate. To summarize, the proposed model is a good candidate for road safety detection.

Uncovering the association between traffic crashes and street-level built-environment features using street view images

Investigating the relationship between built environment factors and roadway safety is crucial for preventing road traffic accidents. Although studies have analyzed traffic-related built environment factors based on pre-determined zonal units, conclusive evidence regarding the relationship between streetscape features and traffic accidents at a fine-grained road segment level is still lacking. With the widespread availability of large-scale street view images, automatically analyzing urban built environments on a large scale is possible. Therefore, the aim of this study was to investigate the relationship between streetscape features and traffic accidents at a fine-grained road segment level using street view images. Specifically, we employed semantic image segmentation to extract streetscape elements from urban street view images, and then created traffic crash-related variables, including the street-level built environment variables, traffic variables, land-use indices, and proximity characteristics, at the road-segment level. Finally, we adopted a classification-then-regression strategy to model the number of traffic crashes while considering the zero-inflated and spatial heterogeneity issues. Our findings suggest that streetscape features can effectively reflect built-environment characteristics at the road-segment level. Moreover, a comparison of our proposed modeling method with existing models demonstrates its superior performance. The results provide insight into the development of effective planning strategies to improve traffic safety.

A Review of Road Traffic Accident Prediction Methods

With the continuous development of urban traffic and the acceleration of urbanization, traffic accidents have become an important issue for urban safety and social stability. In order to prevent and reduce the occurrence of traffic accidents, traffic accident prediction technology has gradually become a hot spot for research. This paper analyzes road traffic accident prediction techniques from articles included in relevant English journals and provides a detailed introduction to the road traffic accident prediction techniques that are already in existence. This paper introduces the current status of research on traffic accident prediction techniques, including traditional statistical analysis methods, machine learning methods, neural network methods, time series analysis methods and techniques based on spatio-temporal data mining, and analyses the advantages and disadvantages of each road traffic accident prediction method. These methods are able to analyse the influencing factors of traffic accidents, build prediction models, improve prediction accuracy and provide strong support for road traffic accident prevention effects for urban traffic safety. Finally, the main difficulties faced in road traffic accident prediction and the future development trend of road traffic accident prediction is discussed. The work done in this paper can provide necessary theoretical support for relevant researchers and save the time needed for literature review.

Emergency Medical Service Response Time for Road Traffic Accidents in the Kingdom of Saudi Arabia: Analysis of National Data (2016–2020)

Despite preventive measures and initiatives, road traffic accidents are on the rise in the Kingdom of Saudi Arabia. This study aimed to investigate the emergency medical service unit's response to RTA by socio-demographic and accident-related variables in the Kingdom of Saudi Arabia. This retrospective survey included Saudi Red Crescent Authority data on road traffic accidents between 2016 and 2020. As part of the study, information on sociodemographic characteristics (e.g., age, sex, and nationality), accident-related data (type and place of the accident), and response time to road traffic accidents were extracted. Our study included 95,372 cases of road traffic accidents recorded by the Saudi Red Crescent Authority in the Kingdom of Saudi Arabia between 2016 and 2020. Descriptive analyses were performed to explore the emergency medical service unit's response time to road traffic accidents, and linear regression analyses were performed to investigate the predictors of response time. Most of the road traffic accident cases were among males (59.1%), and the age group of 25-34 years accounted for about a quarter (24.3%), while the mean age of the road traffic accident cases was 30.13 (±12.86) years. Among the regions, the capital city of Riyadh experienced the highest proportion of road traffic accidents (25.3%). In most road traffic accidents, the mission acceptance time was excellent (0-60 s; 93.7%), movement duration was excellent (<120 s; 91.1%), reaching site duration was excellent (<12 min; 57.9%), treatment start time was excellent (<120 s; 76.4%), duration at the scene was poor (>15 min; 40.8%), reaching hospital duration was good (30-60 min; 52.7%), and in-hospital duration was poor (>15 min; 44.1%). Regions, places and types of accidents, age, gender, and nationality of victims were significantly associated with different parameters of response time. Excellent response time was observed in most of the parameters except the duration at the scene, reaching hospital duration, and in-hospital duration. Apart from the initiatives to prevent road traffic accidents, policymakers should focus on strategies to improve accident response time to save lives.

The Comparison of Aggressive Driving Behavior between Pakistan and China Based on Driver Behavior Questionnaire

Aggressive Driving behavior plays a key role in road safety as it is important in road traffic accident prevention. The aim of this study was to investigate the aggressive driving behavior attitude towards road safety issues in Pakistan and china and the applicability of three-factor structure (aggressive violations, ordinary violations and errors) of both version of driver behavior questionnaire (DBQ). T-test was conducted to evaluate significant differences and it exposed that Chinese sample is more significant. Confirmatory factor analysis was tested and calculated on both samples. Chinese samples fit the CFA statistics models best as compared to Pakistani sample. Additionally, Pakistan drivers especially the younger drivers were mostly engaged in aggression and errors, while Chinese drivers appeared more disciplined and rule followers.

Characterization of Traffic Accidents Based on Long-Horizon Aggregated and Disaggregated Data

For sustainable transportation systems, modeling road traffic accidents is essential in order to formulate measures to reduce their harmful impacts on society. This study investigated the outcomes of using different datasets in traffic accident models with a low number of variables that can be easily manipulated by practitioners. Long-horizon aggregated and disaggregated road traffic accident datasets on Egyptian roads (for five years) were used to compare the model’s fit for different data groups. This study analyzed the results of k-means data clustering and classified the data into groups to compare the fit of the base model (Smeed’s model and different types of regression models). The results emphasized that the aggregated data used had less efficiency compared with the disaggregated data. It was found that the classification of the disaggregated dataset into reasonable groups improved the model’s fit. These findings may help in the better utilization of the available road traffic accident data for determining the best-fitting model that can assist decision-makers to choose suitable road traffic accident prevention measures.

Методика расчета риска получения травм при реконструкции дорожно-транспортных происшествий с участием пешеходов

At present, in road traffic examinations (RTE) carried out in the Russian Federation, there are no modern comprehensive studies, scientific and practical solutions and methods which would be dedicated to assessing the risk of obtaining injuries of varying severity by road traffic participants. In contrast to injury assessment in the development of passive safety measures, injury indices in practice are not used in RTE, which leads to a lack of categorical conclusions in a number of expert tasks. The purpose of this study is to define a methodological approach to the solution of specialized tasks of road traffic accident examination and expert prevention of road traffic accidents in general related to the assessment of the risk of pedestrian injury in a road traffic accident. The article presents the analysis results of road traffic accident examinations. The methodology for evaluating the risk of pedestrian injuries in primary and secondary injuries (when being hit by the vehicle and when falling on the roadway) is outlined. The ways of this methodology`s further use in the practice of road traffic accident examinations, as well as in the development of measures within the expert prevention of traffic accidents, are shown.

Analysis of secondary injury to pedestrians in the tasks of road and transport expertise

The article presents the results of processing a series of numerical experiments to simulate a pedestrian collision, taking into account various parameters of the onset of contact. A logit model of the probability of severe or fatal injury is proposed, taking into account the vehicle speed, the pedestrian speed, the pedestrian mass, and the height ratio of the front edge of the hood to the height of the thigh at the moment of contact. The purpose of this article is to define a methodical approach to the solution of specialized tasks of road traffic accident examination and expert prevention of road traffic accidents in general, related to the assessment of secondary trauma of a pedestrian in a road traffic accident. Keywords: road accident, pedestrian, injury index, second injury, road accident analysis

Current trends of clinico-radiological findings, management and outcomes of chest trauma patients in a tertiary health care hospital in Western India

Background: There has been a rapidly rising trend of trauma cases, especially chest trauma; necessitating an in-depth study of its complications and management.Methods: We have studied 120 patients presenting to our emergency department for the demographics and etiology of their injury and recorded the results and outcomes of their radiological and clinical investigations and management protocol.Results: Middle aged men were the primary victims of chest trauma, mostly having blunt injuries and presenting with respiratory distress. A range of pleural and visceral injuries were seen like hemothorax, pneumothorax, flail chest; the mainstay of treatment for them being minimally invasive tube thoracostomy. We encountered a low mortality rate of 1.66%.Conclusions: Rapid intervention and resuscitation for all trauma cases prove to be lifesaving, amongst which focus on thoracic injuries must be primary. Ever increasing incidences of polytrauma cases indicate the requirement of strict vigilance and prevention of road traffic accidents.

Kiến thức, thái độ, thực hành phòng tránh tai nạn giao thông đường bộ ở học sinh trường trung học phổ thông Hoài Đức A, thành phố Hà Nội, năm 2021

Objective: Describe the knowledge, attitude and practice of road traffic accident prevention among students at Hoai Duc A High School, Hanoi, in 2021. Research Methods: Cross-sectional descriptive. An online survey was conducted by 694 students of Hoai Duc A High School through a data collection toolkit designed on Google Forms software from April 2021 to December 2021. Select 18 investigators who are 18 homeroom teachers of the classes participating in the study. The investigator sent the online survey's link to students who agreed to participate in the study via the classroom email. During the process, the researcher and the investigator answered the students' questions by phone and reminded the students to complete 100% of the questionnaire. Results: Among 694 students, the average knowledge score was 7.73 (SD: 1.84). The percentage of students participating in the study with correct knowledge about road traffic accident prevention (road traffic accident) was 58.1%. The average score of attitude through 15 evaluation questions reached 59.46 (SD: 8.78). There was 76.2% of students have a positive attitude about road traffic accident prevention. Analysis of the percentage of students practicing road traffic accident prevention at 4 positions when participating in traffic shows the following results: 67.6% of students practice correctly when walking; 42.9% of students practice correctly when riding bicycles; 37% of students practice correctly when operating motorbikes/electric mopeds/electric bicycles; 40.7% of students practice correctly when sitting behind motorbikes/electric bicycles/bicycles. The percentage of students who practice correctly about road traffic accident prevention at all positions when participating in traffic was 34.5%. Conclusion: The percentage of students in the study with correct knowledge about road traffic accident prevention was not high with 58.1%. However, the majority of students (76.2%) had a positive attitude about road traffic accident prevention. The percentage of students who correctly practice about traffic accident prevention at all traffic traffic locations is quite low, only 34.5%. It is necessary to organize a variety of traffic safety communication programs in more attractive and appropriate forms that combine theory and practice at school and in the locality. students' level and practice of road traffic accident prevention. Keywords: road traffic accident, high school students, road traffic accident prevention, KAP road traffic accident prevention

Exploration of Road Safety Measures to Prevent Road Traffic Fatalities in South Africa

Road traffic accidents is a global problem where road users are losing their lives on the road even in South Africa is prevalent. Compliance to the rules of the road is important aspect of road safety and road users should play their part on the road. RTA is a collective responsibility and law enforcement agencies should lead this important task to ensure that road users are safe whenever they are on the road. The inability of law enforcement agencies to formulate road safety measures to address road traffic fatalities is a major problem in South Africa. This article focused on road safety measures to curb factors contributing to road traffic accidents in South Africa. The article is based on a qualitatively rooted methodology including a wide range of primary and secondary sources focusing on road safety measures to overcome road traffic accidents. The article further draws on the findings from the world on road safety measures and formulate steps to counteract road traffic accidents in South Africa. Road safety measures could play a critical role in changing the behaviour of road users other than just focusing on reducing road traffic accidents. The research based key steps leading to the prevention of road traffic accidents. The first step related to the process of raising awareness in road safety; capacitate law enforcement agencies with resources to address non-compliance on the road and lastly to strengthen partnerships in road safety to counteract road traffic fatalities.

Assessment of Pattern of Abdominal Injury over a Two-Year Period at St Paul's Hospital Millenium Medical College and AaBET Hospital, Addis Ababa, Ethiopia: A Retrospective Study.

Background Globally, injury continues to be an important cause of morbidity and mortality both in developed and developing countries. Abdominal injuries are among the major causes of trauma admissions. This study aimed to assess patterns of abdominal injury at AaBET and St. Paul's Hospital Millennium Medical College. Methods A cross-sectional study was done on all adult patients who sustained abdominal injuries presented to the emergency department and managed at AaBET and St. Paul's Hospital Millennium Medical College over a two-year period from January 2018 to December 2019. Results A total of 165 abdominal injured patients presented during the study period. Among those patients, 140 (84.8%) were male, with a male-to-female ratio of 5.61. The mean age of patients was 29.3 years. 85 (51.5%) of the patients sustained penetrating injuries. 53 (32.1%) patients sustained road traffic accidents, 47 (32.1%) had stab injuries, and 34 (20.6%) had gunshots. Thirty-four (20.6%) of the patients were managed conservatively and 79.4% (n = 131) were managed surgically. The commonest complications found were shock (n = 20 (12.1%)), peritonitis (n = 18(10.9%)), HAP (n = 9 (5.5%)), and surgical site infection (n = 4 (2.4%)).The mortality rate was 3.6% (n = 6), of which 4 (67%) had the penetrating mechanism of injury. Conclusion Abdominal trauma predominantly affects the male and economically productive age. The three main causes of abdominal injuries in this study were road traffic accidents, stab injuries, and gunshots, which require increased public awareness of the need to prevent road traffic accidents and to handle weapons and sharp items properly.

Exploration of Distracted Road Users in Road Traffic Accidents in South Africa

Distracted road users contribute to high rate of road traffic accidents globally and it is also prevalent in South Africa where many road users lose their lives on the road due to distraction. The inability of road users to adhere to the rules of the road with regard to the use of cell phones, driving while impaired, walking while impaired and all other means of distraction while on the road is a major problem in South Africa. Road users should be focused on the road and follow the rules of the road and avoid other aspects which could derail their journey and lead up to fatality as it is the case on South African roads. This article focused on distracted road users who contribute to high rate of road traffic accidents in South Africa. The article is based on a qualitatively rooted methodology including a wide range of primary and secondary sources. The article further draws on the findings from the world on road safety and formulate steps to road traffic accidents as a result of being distracted on the road. The research based key steps leading to prevention of road traffic accidents as a result of being distracted on the road in South Africa.

Commercial motorcyclists and road safety measures compliance. A case study of Dodoma city, central Tanzania

ObjectiveRoad traffic accidents have been reported to contribute a greater proportion of deaths. Motorcyclists are among the high-risk group within road users to succumb to road traffic accidents. Road traffic accidents reflect the co-occurrence of multiple causes that involve road infrastructure, vehicle, and people. Understanding the level of compliance with road safety measures and their associated factors among commercial motorcyclists is important approach in the steps towards road traffic accident prevention. Study designA cross-sectional study of commercial motorcyclists from two districts in Dodoma region was conducted in 2020. MethodsMultistage sampling method was used to recruit motorcyclists from registered parking stations. At first stage, purposive sampling was used to select the two districts, while in the second stage simple random sampling by lottery method was employed in the selection of wards and parking stations. Descriptive and Chi-square test analyses determined the compliance level. Bivariate and multivariate logistic regression analyses were performed to determine the associated factors. ResultsA total of 378 male commercial motorcyclists participated in the study. Majority (87.8%) were within the age range of 18–29 years. A significant proportion of participants (61.9%) had adequate knowledge and 59.5% showed positive attitude towards road safety measures. Only 39.7% reported to have good level of compliance with road safety measures. Married motorcyclists and those with good road safety practice regarding road safety measures were more likely to comply with road safety measures than their counterpart. ConclusionGood road safety practices and marital status were predictors of good compliance with road safety measures. Coordinated approaches, including continuing provision of road safety education and enforcement of road safety measures are needed to control the way motorcyclist behave on the road.