Rate Of Road Accidents Research Articles

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

Evolution of work conditions for employees driving for work in France based on data from the 2003, 2010 and 2017 SUMER surveys (Surveillance Médicale des expositions aux risques Professionnels)

Introduction: The occupational road-accident risk on public roads and the work conditions for professional driving is still an important issue in occupational health despite lower road-accident rates. This study presents the evolution over time of the work-related constraints for these employees based on the Sumer surveys carried out in 2003, 2010 and 2017. Method: Data from the 2010 and 2017 surveys were restricted to match the scope of the 2003 survey in order to enable prevalence data to be compared in equivalent populations. The main variable of interest was “driving (car, truck, bus, and other vehicles) on public thoroughfares” for work (during the last week of work: yes/no). Work time characteristics, work rhythm, autonomy and scope for initiative, collective work group, standards and evaluations variables were completed by the occupational health physicians. A self-administered questionnaire was also provided to employees and contained the Job Content Questionnaire, which assesses decision latitude, social support and psychological demands, the reward scale of Siegrist questionnaire, the hostile behaviour with inspired questions for Leymann, sick leave and work accidents during the past 12 months and job satisfaction. Finally, prevention in the workplace was also completed by the occupational health physicians. Results: About 25% of employees in France were exposed to work-related driving in 2017, which was stable in comparison with 2003 and 2010. However, the population was older and there were more females, more often from the clerical staff/middle manager category and working in companies with fewer than 10 employees. Employees exposed to work-related driving were also more frequently exposed to sustained work schedules and physical constraints, but less exposed to psychosocial risks. Conclusions: The percentage of employees exposed to occupational road accident risk, i.e., exposure to work-related driving, remained stable at about 25% in 2017 compared with previous surveys. These employees were also more frequently exposed to sustained work schedules and physical constraints, but less exposed to psychosocial risks. Practical Applications: Prevention campaigns on work-related road accident risk should be provided to all employees in all companies since all jobs can be concerned.

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

Variabilele demografice și șofatul agresiv

Drivers in traffic have been showing an increase in aggressive behavior in recent years. The need to study the causes of road accidents and develop measures to mitigate them is confirmed by the worrying statistical data at international and national level on the rate of road accidents. This article reflects the results of an experimental study on the relationship between demographic factors and their influence on aggressive driving undertaken on 204 drivers. The experimentally verified hypothesis was that there are statistically significant differences in aggressive behavior manifested in traffic (aggressive driving, dangerous driving, risky driving) depending on gender, age, background, and socioeconomic status.

Stimulant Drug Using, Road Traffic Accidents, and Illegal Activities among Highway Drivers in Iraq in 2022: A Nation-Wide Cross-Sectional Survey

Background: Drug and stimulant abuse is especially common in low and middle- income countries, such as Iraq and certain populations, such as highway drivers. Moreover, Iraq has high rates of traffic and road accidents. Objective: Our objective was to assess the associations of using stimulants with traffic events among highway drivers in Iraq. Methods: We used a cross-sectional study design, in which a self-administered questionnaire was distributed online on different social media platforms. The study included male highway drivers who used drugs from all provinces of Iraq. The questionnaire consisted of demographic questions and an addiction questionnaire about stimulants specially validated for the Arabic language. We followed a convenience sampling method to recruit participants. Results: A total of 1700 participants from 18 provinces were included with a median (IQR) age of 34 (24:55) years. Around 58% used drugs for reasons other than medical indications, while 81% reported abusing prescription drugs. Amphetamine and cocaine were used by 52% and 20% of the participants, respectively. Around 84% of the drivers had at least 56 driving hours per week, while 87% were involved in illegal activities due to drug use. Most of the participants (78%) had accidents at least three times per month. Most of the participants had social and medical issues. Conclusion: Drug use, especially stimulant use, is a major contributor to road accidents and thus, increased morbidity and mortality rates in Iraq. Urgent policies and regulations are needed regarding the dispensing of drugs and the working hours of the drivers.

Risky behaviors and road safety: An exploration of age and gender influences on road accident rates.

Human behavior is a dominant factor in road accidents, contributing to more than 70% of such incidents. However, gathering detailed data on individual drivers' behavior is a significant challenge in the field of road safety. As a result, researchers often narrow the scope of their studies thus limiting the generalizability of their findings. Our study aims to address this issue by identifying demographic-related variables and their indirect effects on road accident frequency. The theoretical basis is set through existing literature linking demographics to risky driving behavior and through the concept of "close to home" effect, finding that the upwards of 62% of accidents happen within 11km of a driver's home. Using regression-based machine learning models, our study, looking at England, UK, explores the theoretical linkages between demographics of an area and road accident frequency, finding that census data is able to explain over 28% of the variance in road accident rates per capita. While not replacing more in-depth research on driver behavior, this research validates trends found in the literature through the use of widely available data with the use of novel methods. The results of this study support the use of demographic data from the national census that is obtainable at a large spatial and temporal scale to estimate road accident risks; additionally, it demonstrates a methodology to further explore potential indirect relationships and proxies between behaviors and road accident risk.

Selenium alleviates modafinil-induced neurobehavioral toxicity in rat via PI3K/Akt/mTOR/GSK3B signaling pathway and suppression of oxidative stress and apoptosis: in vivo and in silico study.

Nonmedical use of modafinil (MOD) led to increased rates of overdose toxicity, road accidents, addiction, withdrawal, suicide, and mental illnesses. The current study aims to determine the probable MOD brain toxicity and elucidate the possible role of selenium (Se) in ameliorating the neurotoxicity in rat models. Fifty-four male Albino rats were randomly assigned into nine groups. The groups were G1 (control negative), G2 (Se0.1), G3 (Se0.2), G4 (MOD300), G5 (MOD600), G6 (Se0.1 + MOD300), G7 (Se0.2 + MOD300), G8 (Se0.1 + MOD600), and G9 (Se0.2 + MOD600). After finishing the experiment, blood and brain tissue were harvested for biochemical and histological investigation. Neurobehavior parameters were assessed. Tissue neurotransmitter levels and oxidative stress markers were assessed. Gene expression of PI3K/Akt/mTOR-GSK3B, orexin, and orexin receptor2 was measured by qRT-PCR. Histological and immunohistochemistry assessments, as well as molecular docking, were carried out. MOD-induced neurobehavioral toxicity exhibited by behavioral and cognitive function impairments, which are associated with decreased antioxidant activities, increased MDA levels, and decreases in neurotransmitter levels. Brain levels of mRNA expression of PI3K, Akt, and mTOR were decreased, while GS3K, orexin, and orexin receptors were significantly elevated. These disturbances were confirmed by histopathological brain changes with increased silver and Bax immunostaining and decreased crystal violet levels. MOD induced neurotoxic effects in a dose-dependent manner. Compared with the MOD groups, SE coadministration significantly attenuates MOD-induced toxic changes. Docking study shows the protective role of Se as an apoptosis inhibitor and inflammation inhibitor. In conclusion, Se could be used as a biologically effective antioxidant compound to protect from MOD neurobehavioral toxicity in Wistar rats by reversing behavioral alterations, inflammation, apoptosis, and oxidative injury.

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management.

Revving Up Risk: The Surge of Superbikes in India and its Impact on Road Traffic Accidents

Abstract In 2021, India recorded over 412,000 accidents, causing nearly 154,000 deaths, with the 18-45 age groups being most affected. The Ministry of Road Transport and Highways has implemented various measures, including vehicle and road improvements, educational campaigns, and the 2019 Motor Vehicle (Amendment) Act. They’re also employing technology like Integrated Road Accident Database (iRAD) and e-Detailed Accident Report (eDAR) for real-time accident analysis. The subsequent section highlights the significant growth of the luxury motorcycle market in India. Factors driving this include rising disposable income, a shift in perception of superbikes as a lifestyle statement, expanded exposure through events and media, enhanced dealership networks, and customization options. The popularity of long-distance touring and adventure riding further fuels this trend. However, a disparity exists between the rise of superbikes and road development. Poor road conditions, traffic congestion, and a lack of safety measures hinder the riding experience. Additionally, limited access to remote areas and insufficient road safety education contribute to the problem. The anticipated issue is the escalating rate of road accidents, with vulnerable road users at heightened risk. The report also outlines common injuries from motorcycle accidents, ranging from head injuries to psychological trauma. The way forward advocates for cautious riding in India, given existing road conditions. Manufacturers are urged to promote road safety, provide insurance, and collaborate with organizations for road maintenance. Riders pursuing adventure or off-road experiences should prioritize safety by appropriately equipping their superbikes. At a policy level, initiatives like roadside warnings and cardiopulmonary resuscitation (CPR) training for traffic police can significantly enhance road safety. Overall, synchronized development between high-end superbikes and road infrastructure is crucial to curbing further casualties and disabilities in India.

TO EVALUATE THE VARIOUS RISK FACTORS RESPONSIBLE FOR ROAD TRAFFIC ACCIDENTS IN PUNJAB – A DESCRIPTIVE STUDY

Objective: Evidence from research suggests that human factors and the road environment are among the most frequent contributors to the occurrence of road traffic accidents (RTAs). Hence, traffic rule and various policies, alike have strengthened their efforts to cope with this public health burden. Yet, still little is known about the factors contributing to the high number of RTAs. In this study, we tried to find out the various risk factors responsible for road accidents in the Punjab region. Methods: The present study was conducted in Guru Nanak Dev Hospital, Amritsar, involving 1425 patients who were admitted to the Emergency Department from January 1st, 2012, to December 31st, 2012, due to injuries in RTAs. Results: In our study, it seems that most of the cases were in the age group of 15–44 years. In all the age groups, males were the predominant victims with male-to-female ratio of 5.72:1. Majority of cases belonged to an upper–lower socioeconomic group, according to the Kuppuswamy scale and the major proportion of accidents occurred during the morning and evening busy hours of the day hours. Out of a total of 893 drivers, 858 (96.08%) had no knowledge about the speed limit prescribed for the road or speed. Conclusion: There appears to be a large number of behavioral issues and a lack of knowledge that contributes significantly to the road accident rates in the Indian states. Hence, there is a need for policy intervention. Most effective measures used to reduce the number of accidents, in descending order of magnitude, are how strictly the traffic police enforce the law, more levels of penalties, and more traffic police, including more speed measuring devices.

Modelling and simulation of automatic brake system of vehicle

The increasing rate of road accidents is alarming and any vehicle that does not have an effective braking system is vulnerable to an accident with human and property consequences. This is due to human error when driving, involving reaction time delays and loss of concentration. An automatic braking system is an active safety system equipped on modern cars to minimize the possibility of a collision while participating in traffic. The automatic braking system helps warn the driver of an impending collision and helps the driver brake with maximum force or automatically brake the car in an emergency situation. This study analyzes the working principle of the automatic braking system, designs an obstacle detection model using ultrasonic sensors, and models an automatic braking system using the rules in the controller. Fuzzy logic for the system for anti-lock braking detection and model simulation has been developed using Matlab - Simulink software to achieve high braking torque, optimum slip ratio, distance, and stopping time in shorter car. The results show that the distance and stopping time are improved compared to when the controller is not used.

Information and Entropy Aspects of the Specifics of Regional Road Traffic Accident Rate in Russia

The aim of this research is to study the specifics of the road accident rate formation processes in regions of the Russian Federation (2021) using information-entropic analysis. The typical research approaches (correlation-regression, factorial analyses, simulation modelling, etc.) do not always allow us to identify its specificity. It is impossible to evaluate the quality of the researched process’s structure using these methods. However, this knowledge is required to understand the distinctions between high-quality road safety management and its opposite. In order to achieve the goal of the research methodology based on the use of the classical approaches of C. Shannon, the quantitative value of information entropy H was elaborated. The key components of this method are the modelling of the cause-and-effect chain of road accident rate formation and the consideration of the relative significances of individual blocks of the process in achieving the final result. During the research the required statistical data were collected and the structure of the road accident rate formation process in 82 regions of the Russian Federation in the format “Population P—Fleet of vehicles NVh—Road Traffic Accidents NRA—RTA Victims NV—Fatality Cases ND” was analyzed. The fact that the structure of the road accident rate formation process is extremely specific in different Russian regions was shown. Exactly this specificity forms the degree of ambiguity in the state of Russian regional road safety provision systems in terms of the probability of death in road accidents. The main conclusion of this research is that information-entropic analysis can be successfully used to assess the structural quality of road safety systems.

Detecting Driver Sleepiness using Convolutional Neural Networks

The development in computer vision has aided drivers in the form of automatic self-driving cars etc. The accidents are caused by driver's exhaustion and drowsiness about 20%. Its carriages a dangerous issue for which numerous methods were proposed. However, they are not appropriate for real-time implementation. The major encounters confronted by these approaches are forcefulness to handle dissimilarity in human face and lightning conditions. Our intention is to implement a smart operating system that can lower the rate of road accidents considerably. This method enables us to find driver's face features like eye closure percentage, eye-mouth aspect ratios, blink rate, yawning, head movement, etc. In this classification, the driver is uninterruptedly observed by using a webcam. The car driver’s facial features along with the eye movements are observed using a cascade classifier. Eye images are pull out and fed to Custom designed Convolutional Neural Network for categorizing whether both left and right eye are closed. Based on the sorting, the eye closure score is considered. Upon finding that the driver is being detected drowsy that a high alarm will be raised.

The impact of politics, security and health on the escalation of road traffic accidents and their consequences in Iraq for the period 2015-2020.

This study aimed to detect the impact of politics, security and health on the escalation of road traffic accidents (RTAs) and their consequences in Iraq for the period 2015-2020. The data of this cross-sectional study were obtained from the annual reports of RTA statistics from the Central Statistical Organisation of the Iraqi Ministry of Planning. The statistical analysis of data was done using the Statistical Package for the Social Sciences. In this study, it was noted that the rate of RTAs, injuries and deaths increased from 16% to 19.5%, from 16.5% to 17.3% and from 16.2% to 20%, respectively, for the years 2015-2019, while the rates of RTAs, injuries and deaths in 2020 recorded a clear decrease compared to the rest of the years under study, and the rates were as follows: 14.8%, 14.4% and 14.1%, respectively. According to the types of traffic accidents, the rate of crashes recorded the highest rate of 51% in all years of the study compared to the run over, overturn and other unclassified accidents, which were 37%, 10% and 2%, respectively, and it was revealed that crashes, run over and other unclassified incidents increased from 5% to 20%, from 17% to 19% and from 18% to 26%, respectively, for the years 2015-2019, whereas all types of RTAs in 2020 were recorded the lowest rates compared to other years under study. Al-Basra, Baghdad, Al-Najaf and Babylon were recorded the highest rates of traffic accidents for the 6 years under study, with a rate of 13.3%, 11.7%, 11.1% and 10.4%, respectively. Although traffic accidents were not recorded in the years 2015, 2016 and 2017 for the governorates of Nineveh and Anbar due to the occupation of the terrorist Islamic State, traffic accidents maintained their levels due to internal migration. It was also noted that the rate of road accidents was the highest in 2019 compared to the years under study due to the liberation of occupied cities, the return of safety and the return of recording accident data again, while the year 2020 recorded a clear decrease in the rate of road accidents, especially in the first half of it due to the October protests that led to the suspension of all government institutions, universities and schools. The second reason for the decrease in the second half of 2020 is attributed to the lockdown of corona. It seems that the political, security, military and health factors have an effective impact on the traffic reality.

Forecasting the road accident rate and the impact of the covid 19 on its frequency in the polish provinces

The COVID-19 pandemic has significantly affected the development of road transport, not only in Poland, but also worldwide. Limited mobility, especially at the beginning of the pandemic, had a large impact on the number of road accidents. The aim of the present study is to predict the number of road accidents in Poland and to assess the impact of the COVID-19 pandemic on variability of the number of road accidents. To attain the objective, annual data on the road accidents for every province in Poland were collected and analysed. Based on historical crash data, obtained from the police, the number of road accidents was forecasted for both pandemic and non-pandemic scenarios. Selected time series models and exponential models were used to forecast the number of accidents.

SMART IGNITION USING HELMET DETECTION

Over the past few decades, the percentage of individual transportation has increased from 39.2% to 77% globally, which shows rapid growth in the sales of Automotive Industries, as this increases, it also shows an increased rate of road accidents. The goal of this project is to use the Raspberry Pi, for the envisioned advanced system. The Raspberry Pi will serve as a command module. This project aims to introduce an efficient and feasible methodology by implementing Object Detection for improved safety. The project incorporates image processing where the authorized user’s wearing a helmet are recognized and verified with the database and allows users to turn on the Ignition System. Implementation of this system can be done on motorcycles with minimal installation cost which provides safety to the users. Keeping in mind the future perspectives, Smart Ignition Using Helmet Detection can surely provide a better system thereby ensuring passenger safety.

Factors related to healthcare costs of road traffic accidents in Bucaramanga, Colombia.

ABSTRACTOBJECTIVE To determine the factors related to overall healthcare costs of road traffic accidents in Bucaramanga, Colombia.METHODS A descriptive cross-sectional study was conducted through the analysis of road traffic accident records that took place in 2019 in Bucaramanga, Colombia. Cost quartiles in dollars were compared using Pearson’s chi-squared and Fisher’s exact tests. Odds ratios were also calculated in logistic regression.RESULTS 3,150 road accidents were reported in 2019 involving 7,038 people, of which 812 had information related to healthcare costs in health care institutions. The median cost was 56.59 USD (RI = 29.35–140.15), average cost of 290.11 USD ± 731.22 (95%CI: 239.74–340.48). A higher possibility to be in the 4th quartile was found when persons were under 18 years of age (OR = 4.88; 95%CI: 1.30–18.32) or 46–60 years (OR = 3.66; 95%CI: 1.01–13.30), the type of vehicle involved is motorcycle (OR = 2.79; 95%CI: 1.25–6.24), bicycle (OR = 7.66; 95%CI: 2.70–21.68), having a head injury (OR = 4.50; 95%CI: 2.61–7.76) and hypothetical drunk driving (OR = 12.44; 95%CI: 2.01–76.87).CONCLUSIONS Relevant factors in healthcare costs were riding a motorcycle or bicycle, having a head injury, being under 18 years of age or 46 to 60 years of age and hypothetical drunk driving. It is important to implement prevention measures based on identified factors to reduce road accident rate and therefore, its socioeconomic costs.

Driver Drowsiness Detection using Opencv and Raspberry Pi

Abstract: Driver drowsiness detection is a step to reduce the rate of road accidents that happen around the world. It integrates the technology of machine learning with computer vision for detection and alert generation. Year by year the degree of fatalities and road accidents has increased due to driver drowsiness and fatigue. The device is robust and cost-effective which can prevent road accidents in most cases. The device has a camera through which it monitors the driver’s eye continuously and processes the image for the sleep onset period. The images taken at random interval is passed through a Neural network that recognizes eye moments and classifies the sleep stage. If the driver is in the initial stage of falling asleep it can alert the driver to wake up and be attentive. If the driver is in a further stage of sleepiness the device will generate an alert to the respective emergency contacts that the driver is on the verge of sleep and they can able to alert the driver in any way. The emergency contacts can also get the location coordinates of the driver from the device's Gps tracking system. One’s inattentiveness driving or careless driving is not only a problem to himself but also to the others on the road, creating a devastating effect. So there is a level of prominence on every solution that can prevent road accidents. Not only the life of drivers but also the people who are dependent on them will have to face the consequences. Keywords: Computer Vision, Raspberry pi, GSM communication, Opencv.

A cranio-encephalic trauma due to electric-scooter accident: could the wearing of a helmet reduce this risk?

Nowadays, one of the most important health and social policy issues concerning all countries is the problem of road accident rates. Traffic is one of the most important risk factors. For this reason, ridesharing companies have been launching electric scooters in Rome since June 2019 with the aim of reducing car traffic. In the absence of relevant legislations, the risk is that of facing an increase in deaths due to electric scooter crashes. We report the case of an electric scooter accident victim with cranio-encephalic trauma associated with limb injuries that caused immediate death. This case report emphasizes how the obligation of using helmets must be extended to all ages, in order to reduce the risk of increasing the number of deaths. Compulsory helmet use can reduce fatalities in all cases where high-speed crashes are not involved.

Prototipe Sistem Deteksi Jarak Kendaraan Roda Empat Untuk Mengurangi Angka Kecelakaan Berbasis Arduino

Based on the research, we need a technology that can be used to monitor the distance when driving in order to reduce the rate of road accidents. The purpose of the author is to make a device on vehicles using Arduino. So the author makes a solution with a report entitled" Design and build a distance detection device on cars to reduce the number of accidents in Lampung based on Arduino". This tool utilizes an ultrasonic sensor as a distance reader,so that the driver can determine the distance between the vehicle and the object in front or behind the vehicle. Based on the results of research that has been tested using the Black box testing method, and the use of the device is in accordance with the function.