Reduce Road Traffic Accidents Research Articles

Magnitude and determinants of road traffic accidents in North Gondar Zone; Amhara Region, Ethiopia

Background Road traffic accidents are a severe health concern in Ethiopia. This study aims to examine the primary contributing variables of road traffic accidents in Ethiopia, North Gondar Zone, under 24 administrative wereda’s. Methods This study utilized secondary data collected from both the werda-level and North Gondar central-level offices within the study areas. The sector police typically submit reports to the zonal-level offices annually. Information regarding crashes, fatalities, and injuries during the study period was extracted from these police reports. The research focused on analyzing the frequency and severity of traffic accidents attributed to various factors of drivers, vehicles, road conditions, pedestrians, and environmental influences. Results In a sample of 603 aggregate road traffic accidents, the data reveals that the majority of damage results are attributed to young drivers, accounting for 58% of the cases, with junior grade level drivers contributing 29.4%. Driving without a license was a significant factor, representing 87%, while accidents involving recruited driver stood at 63.2%. Vehicles with small to articulated trucks contributing to 39%. Mechanical failures were prevalent, with an unknown diagnosis rate of 48.3%. Vehicles in service for 2-5 years accounted for 29.4%. Road surface conditions were 46%, while rural roads saw a higher percentage at 51.2%. Weather conditions played a role also influential, as dry asphalt and gravel roads under main access ownership were linked to of accident, with 91.2% occurring during visible daylight hours between 8:00-12:00 and 15:00-17:00. The victims were predominantly farmers at 49.6%, followed by students at 23%. Conclusions To reduce road traffic accidents in the study area, it is essential to manage causative factors such as driver and road users, vehicles, roads, pedestrians, and environmental behaviors. The study suggests the development of a successful, up-to-date traffic accident database system regionally within respective offices.

Effectiveness of speed cameras on the prevention of road traffic collisions and casualties in cameroon

Issues related to transport collision and casualties cannot be undermined as it has direct effect on human health and death, hence relevant policies are required to reduce road traffic accidents. To do this, our study has as objectives: to discuss the drivers of the used of speed cameras by drivers in Cameroon., investigate the effects of speed cameras on the prevention of road traffic collisions and casualties in Cameroon and to determine the actual problems faced by drivers in the use of speed Cameras in Cameroon. Methodologically, the study applied the probit technique to estimate the result via primary data. The sample size consisted of 200 drivers collected through a well-structured questionnaire and collect among the drivers plying the Yaoundé-Doula, Douala –Bafoussam and Bafousam-Yaoundé major highway. The key findings revealed that the factors influencing the drivers used of speed cameras are: main occupation, attainment of higher education, married, age, victim of traffic collision and casualties. The result also shows that Speed camera is highly corroborating with the prevention of road traffic collisions and casualties in Cameroon. Finally, the result shows that the lack of awareness, student speed change, inadequate signage, limited visibility, technical limitations and privacy concerns are the actual physical challenges of the use of speed camera in Cameroon. The study suggests that decision makers in the transport sector should multiply the provision of speed cameras. This is a wise step in the prevention of road traffic collisions and casualties in Cameroon and better labour market.

A novel perspective on the selection of an effective approach to reduce road traffic accidents under Fermatean fuzzy settings.

Road traffic accidents (RTAs) pose a significant hazard to the security of the general public, especially in developing nations. A daily average of more than three thousand fatalities is recorded worldwide, rating it as the second most prevalent cause of death among people aged 5-29. Precise and reliable decisionmaking techniques are essential for identifying the most effective approach to mitigate road traffic incidents. This research endeavors to investigate this specific concern. The Fermatean fuzzy set (FFS) is a strong and efficient method for addressing ambiguity, particularly when the concept of Pythagorean fuzzy set fails to provide a solution. This research presents two innovative aggregation operators: the Fermatean fuzzy ordered weighted averaging (FFOWA) operator and the Fermatean fuzzy dynamic ordered weighted geometric (FFOWG) operator. The salient characteristics of these operators are discussed and important exceptional scenarios are thoroughly delineated. Furthermore, by implementing the suggested operators, we develop a systematic approach to handle multiple attribute decisionmaking (MADM) scenarios that involve Fermatean fuzzy (FF) data. In order to show the viability of the developed method, we provide a numerical illustration encompassing the determination of the most effective approach to alleviate road traffic accidents. Lastly, we conduct a comparative evaluation of the proposed approach in relation to a number of established methodologies.

Synchronous hybrid brain–computer interfaces for recognizing emergency braking intention

AbstractHybrid neurophysiological signals, such as the combination of electroencephalography (EEG) and electromyography (EMG), can be used to reduce road traffic accidents by obtaining the driver's intentions in advance and accordingly applying appropriate auxiliary controls. However, whether they can be used in combination and can achieve better results in situations of detecting emergency braking from normal driving and soft braking has not been explored. This study used one feature‐level (hybrid BCI‐FL) and three classifier‐level (hybrid BCIs‐CLs) hybrid strategies, the spectral band, and spectral point features to construct recognition models. Offline and pseudo‐online experiments were conducted. The recognition performance with the spectral point features showed a better result than that with spectral band features. In all experiments, the two proposed hybrid BCI strategies could achieve a detection accuracy close to or above 95%, while the detection advanced time is less than 300 ms. In particular, for the developed hybrid BCI recognition models, the hybrid BCI‐FL and hybrid BCI‐CL2 recognition models with spectral point features achieved 4.25% (p < 0.015) and 4.69% (p < 0.006) higher system accuracies, respectively, than that of the current better single EMG‐based recognition model. This research promotes the application of hybrid EEG and EMG signals in intelligent driving assistance systems.

Prediction of the burden of road traffic injuries in Iran by 2030: Prevalence, death, and disability-adjusted life years

PurposeRoad traffic accidents pose a global challenge with substantial human and economic costs. Iran experiences a high incidence of road traffic injuries, leading to a significant burden on society. This study aims to predict the future burden of road traffic injuries in Iran until 2030, providing valuable insights for policy-making and interventions to improve road safety and reduce the associated human and economic costs. MethodsThis analytical study utilized time series models, specifically autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs), to predict the burden of road traffic accidents by analyzing past data to identify patterns and trends in Iran until 2030. The required data related to prevalence, death, and disability-adjusted life years (DALYs) rates were collected from the Institute for Health Metrics and Evaluation database and analyzed using R software and relevant modeling and statistical analysis packages. ResultsBoth prediction models, ARIMA and ANNs indicate that the prevalence rates (per 100,000) of all road traffic injuries, except for motorcyclist road injuries which have an almost flat trend, remaining at around 430, increase by 2030. Based on estimations of both models, the rates of death and DALYs due to motor vehicle and pedestrian road traffic injuries decrease. For motor vehicle road injuries, estimated trends decrease to approximately 520 DALYs and 10 deaths. Also, for pedestrian road injuries these rates reached approximately 300 DALYs and 6 deaths, according to the models. For cyclists and other road traffic injuries, the predicted DALY rates by the ANN model increase to almost 50 and 8, while predictions conducted by the ARIMA model show a static trend, remaining at 40 and approximately 6.5. Moreover, these rates for the prediction of death rate by the ANN model increased to 0.6 and 0.1, while predictions conducted by the ARIMA model show a static trend, remaining at 0.43 and 0.07. According to the ANN model, the predicted rates of DALY and death for motorcyclists decrease to 100 and approximately 2.7, respectively. On the other hand, predictions made by the ARIMA model show a static trend, with rates remaining at 200 and approximately 3.2, respectively. ConclusionThe prevalence of road traffic injuries is predicted to increase, while the death and DALY rates of road traffic injuries show different patterns. Effective intervention programs and safety measures are necessary to prevent and reduce road traffic accidents. Different interventions should be designed and implemented specifically for different groups of pedestrians, cyclists, motorcyclists, and motor vehicle drivers.

Spanish adaptation and validation of the Dula Dangerous Driving Index (DDDI).

The Dula Dangerous Driving Index (DDDI) is a widely used questionnaire that measures the tendency to drive dangerously on the road through three different types of behaviors: aggressive driving, risky driving, and experiencing negative emotions while driving. This study aimed to develop a Spanish version of the DDDI and verify the reliability and validity of this questionnaire in the Spanish population. A community sample of 2174 Spanish participants (51.1% male; age range: 18-79 years) completed the 28-item Spanish version of the DDDI. Confirmatory factor analysis revealed that a three-factor model fitted adequately to the data. Analysis of internal consistency, test-retest reliability, and convergent validity showed that the Spanish adaptation of the DDDI had good psychometric properties and retains the theoretical consistency of the original scale. Gender and age differences were observed. The Spanish version of the DDDI can be considered a good instrument for assessing dangerous driving behavior, thus contributing to the cross-cultural study of these types of behaviors and the possible development of intervention programs aimed at reducing road traffic accidents.

Artificial Intelligence in Road Traffic Accident Prediction

The rapid development of AI shows its power and great development potential in practical engineering applications. Critical issues and potential solutions can reduce road traffic accidents and application of AI in road accident prediction. The published use of AI for road accident prediction is reviewed, presented, and represented as the main objective. The methods are collecting article data, quotations, presentation, and representation. The article data collection was obtained from 671 conference and journal articles in 2019-2023, but the suitability of articles that can be used is 69. Quotation produces a grouping of approaches, models, predictions, and benefits. The presentation showed that most approaches used were machine learning, the most used model was random forest, the prediction was mostly about severity, and the most benefit was about number reduction. Representation produces road accidents and related factors into factors in road accident predictions using artificial intelligence, so strategies and anticipation can be made to overcome them to improve road safety. AI in road accident prediction plays an important role in building predictive models with the hope that road accidents can be identified early, risk factors can be reduced, and effective preventive measures can be taken to improve road safety.

Causes and Strategies for Reducing Road Traffic Accidents in the Bongo District of Ghana

Aim: This study intends to assess drivers’ state of knowledge on the main causes and the best strategies capable of reducing road traffic accidents in the Bongo District of Ghana.
 Study Design: The study was a descriptive design.
 Place and Duration of Study: The study was conducted in the Bongo District of the Upper East Region of Ghana between July 2023 and August 2023.
 Methodology: The researcher conducted a survey involving 100 drivers who were randomly selected from a driver population of 120 in the Bongo District. The data were analyzed using Microsoft Excel [Version 19]. The results of the data analysis were presented as tables and charts.
 Results: The study found that the main causes of road traffic accidents emanate from human error such as low driving skills, excessive speeding, and poor vision of driver. Also, road conditions like bad road networks, no lane markings, and no speed limit signs were found as causal factors of road traffic accidents. Faulty vehicles with causative factors such as defective tyres, brakes, and lights were noted to be also responsible for road accidents. Nevertheless, the least factor was environmental conditions such as bad weather conditions and stray animals being enumerable as environmental conditions which cause road accidents. The study further found that the best strategies for curbing road traffic accidents were enforcement of traffic laws, education and training of drivers, obeying traffic rules by drivers, and maintaining vehicles well.
 Conclusion: The study, therefore, concluded that human factors play a central role in contributing to road accidents and curbing road traffic accidents. The research findings can be used to guide stakeholders in formulating and prioritizing interventions that are more human-centred for the curbing of road traffic accidents in the Bongo District of Ghana.

Predictive Analysis of Road Traffic Accidents in Katsina State, Nigeria Using Machine Learning Algorithms: A Study on Factors and Mitigation Strategies

This study aims to explore the use of machine learning algorithms in predicting the likelihood of road traffic accidents in Katsina State, Nigeria, with the goal of reducing the high rate of road traffic accidents and associated loss of lives and properties. The study collects and analyzes data on road traffic accidents in Katsina State, including the number of accidents, location, time of day, and the type of vehicles involved. Machine learning algorithms such as Decision Trees, Random Forest, and K-Nearest Neighbors, are trained using the data to predict the likelihood of road traffic accidents. The study also identifies the factors that contribute to road traffic accidents in Katsina State by using techniques such as feature selection and correlation analysis, to identify the most important variables. The findings can be used by stakeholders, including the government, law enforcement agencies, and road safety organizations, to develop and implement effective strategies to reducing road traffic accidents in Katsina State. The study utilizes data collected from the Federal Road Safety Corps database in Katsina, Nigeria, and employs data cleaning and feature selection techniques to improve data quality. The Random Forest algorithm achieved the predictive value as 85% while K-Nearest Neighbors (KNN) and Decision Tree algorithms yielded 17% and 42% respectively.

EXPLORING HUMAN-ERROR FACTORS CONTRIBUTING TO MOTORCYCLE ACCIDENTS IN HANOI CITY USING GREY RELATIONAL ANALYSIS

Along with the development of the economy and society and the speedy increasing number of personal vehicles, especially motorcycles, many cities in developing countries like Hanoi (Vietnam) are facing many transportation problems, notably traffic accidents involving motorcyclists. Motorcycle accidents are caused by main human-error factors, and could be considered as the typical grey system with the features of complexity and imperfect information. In this study, a grey relational analysis (GRA) model of the human-error factors is presented to quickly explore the main human-error factors contributing to traffic accidents caused by motorcycles. In this model, the grey relational degree and the grey relational order of each human-error factor also are determined to rank the main contributing human-error factors. Taking the data of road traffic accidents caused by motorcycles in Hanoi, the capital of Vietnam between 2015 and 2017 as experimental data, the grey relational degrees of human-error factors have been analyzed quantitatively through the GRA model. The experimental results show the first three human-error factors include wrong lane shifting, poor road observation, and speeding, respectively. The results also help to conduct effective countermeasures for controlling human errors and reducing road traffic accidents.

Epidemiological Profile of traumatic spinal cord injuries in the National Referral Hospital, Bhutan

Introduction: The number of traumatic spinal cord injuries associated with a fracture is on rise which is compounded by rise in number of motor vehicle accidents in the country. The aim of the study was to assess frequency, cause, pattern, and demographic profile of traumatic spinal cord injuries in the Bhutanese population. Methods: A retrospective study was conducted at National Referral Hospital from 2018 to 2020. Hospital records of patients, treated for traumatic spinal cord injury were reviewed. Results: Of the total of 100 patients with traumatic spinal injury admitted during study period, there were 69 males (69%) and 31 females (31%). The mean age was 44.18 years (±16.625 SD). Falls (61%) was most common cause of spine injury followed by road traffic accident (27%). Lumbar spine injury was most common (42%) followed by cervical spine (31%) and thoracic spine (23%) injuries. Of 100 patients with spinal injury 63 patients did not have associated cord injuries while 37% of patients had associated spinal cord injuries and of those with spinal injuries, 20% of them had complete spinal cord injury (SCI) and 17% had incomplete SCI. Regarding treatment, 42% of spine injury patients underwent surgical procedures while 58% were managed conservatively. The mean length of hospital stay after spinal injury was 6.62 days (± 26.06 SD, range 1-215). Conclusions: The study found that most common cause of spine and associated cord injuries were falls followed by road traffic accidents. By collaborating with traffic and road safety division, our study findings have potential to significantly reduce road traffic accidents and enhance preventive measures. Additionally, we can provide valuable education to families regarding rehabilitation and social support, ultimately leading to improved outcomes and better quality of life for individuals living with spinal cord injuries.

Obstructive Sleep Apnea in Hypertension.

Introduction About one-half of patients who have essential hypertension have obstructive sleep apnea (OSA), and about one-half of patients who have obstructive sleep apnea have essential hypertension. OSA can cause even resistant hypertension if left untreated. These two entities often co-exist and are seen as a continuum of the same process. Eighty percent to 90% of OSA cases are undiagnosed mostly because of a lack of awareness. Material and methods This was a cross-sectional studydone over a period of one year in a tertiary care hospital. After taking informed consent, 179 hypertensive patients of >18 years were included in the study. All patients were screened for OSA by the STOP-BANG questionnaire. Patients having scores of ≥3were subjected to overnight polysomnography to confirm the diagnosis of OSA (AHI ≥5). Patients with a STOP-BANG score ≤2 or score ≥3 with AHI <5, were considered non-OSA. Results More than half (53.1%) of the patients enrolled in the study had OSA. Their age ranged from 18 to 78 years and the mean age was 52.07±11.40 years. The mean age of OSA cases was found to be slightly higher than that of non-OSA. The majority of OSA cases were males (73.7%). There was an increase in the prevalence, as well as the severity of OSA, with an increase in BMI. Most of the cases had snoring and a history of tiredness. Triglyceride (TG) and low-density lipoprotein (LDL) levels of the OSA group were found to be significantly higher and high-density lipoprotein (HDL) levels were significantly lower than that of the non-OSA group. Conclusion More than half of our hypertensive patients had OSA. These two conditions often co-exist and are known as a dangerous pair. Physicians ought to have increased suspicion for early diagnosis and treatment to improve cardiovascular outcomes, reduce road traffic accidents, and improve quality of life.

Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone

Millions of people are dying, and billions of properties are damaged by road traffic accidents each year worldwide. In the case of our country Ethiopia, the effect of traffic accidents is even more by causing injuries, death, and property damage. Forecasting road traffic accident and predicting the severity of road traffic accident contributes a role indirectly in reducing road traffic accidents. This study deals with forecasting the number of accident and prediction of the severity of an accident in the Oromia Special Zone using deep artificial neural network models. Around 6170 road traffic accidents data are collected from Oromia Police Commission Excel data and Oromia Special Zone Traffic Police Department; the dataset consists of accidents in the Special Zone of Oromia Districts (Woredas) from 2005 to 2012 with 15 accidents attributes. 5928 or (80%) of the dataset was used for the training model, and 1482 or (20%) of the dataset was used for the testing model. This study proposed six different neural network architectures such as backpropagation neural network (BPNN), feedforward neural network (FFNN), multilayer perceptron neural network (MLPNN), recurrent neural networks (RNN), radial basis function neural network (RBFNN) and long short-term memory (LSTM) models for accident severity prediction and the LSTM model for a time serious forecasting of number accidents within specified years. The models will take input data, classify accidents, and predict the severity of an accident. Accident predictor GUI has been created using Python Tkinter library for easy accident severity prediction. According to the model performance results, RNN model showed the best prediction accuracy of 97.18%, whereas MLP, LTSM, RBFNN, FFNN, and BPNN models showed the accuracy of 97.13%, 91.00%, 87.00%, 80.56%, and 77.26%, respectively. LTSM model forecasted accident for three years which is 3555 where the actual accident number is 3561. The prediction and forecast result obtained from the model will be helpful in planning and management of road traffic accidents.

Impact of feedback content on dangerous driving behaviours based on the triangle model of responsibility

The serious problem of road traffic accidents is in urgent need of coping strategies, among which feedback has proven to be helpful. The main purpose of this study is to explore effective feedback content and the mediating effect of drivers’ responsibility based on the triangle model of responsibility. We designed a mixed study with 3 driving scenarios (getting too close to lead vehicle, speeding, and running a yellow light) and 4 feedbacks (no feedback, feedback with only a prescription, feedback with only an event, feedback with both prescription and event) as within-subject factors and 2 driver identities (feedback without driver identity, feedback with driver identity) as a between-subjects factor. A total of 110 Chinese drivers completed this study. The results showed that providing feedback enhanced drivers’ willingness to drive safely, while the effects of feedback with different contents varied with scenario. In addition, the mediating effect of drivers’ responsibility between feedback and speeding was confirmed. This study revealed that the effectiveness of feedback would be significantly influenced by a subtle change in feedback content in different driving scenarios, suggesting the importance of tailoring feedback content according to specific situations. Additionally, we provided a possible explanation of the mechanism of feedback by verifying the mediating effect of responsibility. Our findings of effective feedback contents corresponding to specific driving scenarios could be used to design driving feedback in safety warning systems to improve the effectiveness of such systems in facilitating safe driving behaviour and reducing road traffic accidents.

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.

A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems

Road transport is the most prone to accidents, resulting in significant fatalities and injuries. It also faces a plethora of never-ending problems, such as the frequent loss of lives and valuables during an accident. Appropriate actions need to be taken to address these problems, such as the establishment of an automatic incident detection system using artificial intelligence and machine learning. This article explores the overview of artificial intelligence and machine learning in facilitating automatic incident detector systems to decrease road accidents. The study examines the critical problems and potential remedies for reducing road traffic accidents and the application of artificial intelligence and machine learning in road transportation systems. More, new, and emerging trends that reduce frequent accidents in the transportation sector are discussed extensively. Specifically, the study organized the following sub-topics: an incident detector with machine learning and artificial intelligence and road management with machine learning and artificial intelligence. Additionally, safety is the primary concern of road transport; the internet of vehicles and vehicle ad hoc networks, including the use of wireless communication technologies such as 5G wireless networks and the use of machine learning and artificial intelligence for road transportation systems planning, are elaborated. Key findings from the review indicate that route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management are critical to safeguarding road transportation systems. Finally, the paper summarizes the challenges facing the application of artificial intelligence in road transport systems, highlights the research trends, identifies the unresolved questions, and highlights the essential research takeaways. The work can serve as reference material for road transport system planning and management.

Research on Lane Changing Game and Behavioral Decision Making Based on Driving Styles and Micro-Interaction Behaviors.

Autonomous driving technology plays an essential role in reducing road traffic accidents and ensuring more convenience while driving, so it has been widely studied in industrial and academic communities. The lane-changing decision-making process is challenging but critical for ensuring autonomous vehicles’ (AVs) safe and smooth maneuvering. This paper presents a closed-loop lane-changing behavioral decision-making framework suitable for AVs in fully autonomous driving environments to achieve both safety and high efficiency. The framework is based on a complete information non-cooperative game theory. Moreover, we attempt to introduce human driver-specific driving styles (reflected by aggressiveness types) and micro-interaction behaviors for both sides of the game in this model, enabling users to understand, adapt, and utilize intelligent lane-changing techniques. Additionally, a model predictive control controller based on the host-vehicle (HV) driving risk field (DRF) is proposed. The controller’s optimizer is used to find the optimal path with the lowest driving risk by using its optimizer and simultaneously adjusting its control variables to track the path. The method can synchronize path planning and motion control and provide real-time vehicle state feedback to the decision-making module. Simulations in several typical traffic scenarios demonstrate the effectiveness of the proposed method.

Road Safety Analysis of Autonomous Vehicles

For the widespread use of Autonomous Vehicles (AVs), a huge number of challenges must be solved by vehicle manufacturers, in contrast they do have significant potential to increase road safety in both passenger and freight transport. In addition to reducing road traffic accidents and traffic jams, AVs also offer a major opportunity to reduce pollutant emissions and CO2 emissions from environmental point of view. In order to implement accident-free traffic, also called Vision Zero, it is essential to examine the safety and reliability of AVs. This article analyzes road traffic accident data and the potential safety benefits of AVs. Furthermore, the paper also sets the safety of the conventional vehicles against AVs and examines the type, location, causes, and dynamics of the accidents. The article also provides an overview over the current development trends and challenges, such as the risk of cyber-attacks, the necessary improvements in sensing technologies, and the not insignificant moral issue of AVs.

Analysing factors contributing to road traffic accidents in South Africa

Road Traffic accidents is a major problem worldwide even in South Africa is a leading killer on the road. Human factor is a main contributory factor to fatalities on the road as many road users are not adhering to the rules of the road. The inability of road users to understand and interpret the rules of the road is a major problem on South African roads. This article focus on preventive 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 such as documents from Road Traffic Management Corporation, academic books, articles, dissertations, thesis and reports from non-governmental organisations focusing on road safety. The article further draws on the findings from the world on road safety and formulate steps to counteract road traffic accidents in South Africa. Awareness of road safety play a critical component in changing the behaviour of the 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 increasing law enforcement officials on the road; awareness campaigns to change the behaviour of road users; capacitate law enforcement agencies with resources to address non-compliance on the road; provide anti- corruption strategies in the law enforcement agencies.

Проблемные вопросы использования спутниковой навигации при оценке состояния факторов «дорога» и «среда» в системе ВАДС

The main indicator characterizing the features and specifics of road traffic is the dynamic system «Driver - Car - Road - Environment» (DCRE), in which there is a continuous exchange of information between its components. The level of road traffic safety directly depends on the reliability of the system elements, as well as the specifics of their internal interaction with each other. The article provides an assessment of the factors affecting the reliability of the DCRE system. The authors substantiate the necessity of improving the quality of assessing the key factors of the subsystems «Road» and «Environment» in order to ensure road traffic safety of the motor transport complex. There has been carried out an analysis of the methodology of conducting field surveys in determining the transport and operational qualities of the elements of the «Road» subsystem to ensure the road traffic safety, taking into account the integral components of the DCRE system. The main problems of obtaining high-precision planned coordinates during the operation of road equipment are determined. The possibilities of using RTK (real-time kinematics) and VBS (virtual base station) technologies have been studied in order to improve the methodological apparatus for implementing road safety examinations. The results of the study allow increasing the potential for further work in the field of improving road traffic safety and reducing road traffic accidents` rate, taking into account the road environment features.