Occurrence Of Traffic Accidents Research Articles

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

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

Traffic signal image detection technology based on YOLO

The detection and recognition of traffic signal image is an important content in intelligent transportation system. It can be applied to driver assistance system to effectively recognize traffic signal signs on the road, so as to reduce the occurrence of traffic accidents. At the same time, it also provides strong technical support for the future unmanned driving system. The main content of this paper is based on the deep learning method, using YOLOv3 and YOLOv4 algorithm to detect and recognize the traffic signal image on the road. The experimental results show that the recognition result of YOLOv4 network is better than that of YOLOv3 network.

Empirical Analysis on Influencing Factors of Traffic Casualties in China

In order to further explore the complex correlation between the influencing factors of road traffic accidents in China, this paper takes the road traffic safety-related data of 31 provinces from 2004 to 2018 as the sample data, selects five index data that may affect the occurrence of traffic accidents in various regions of the country, i.e. resident population, urban population, population density, GDP and number of car drivers, and uses Eviews software for stepwise regression analysis to establish a fixed effect model of the influencing factors of traffic accidents and analyze the macro and micro factors that affect the death toll of traffic accidents in China. The model shows that each index has a significant impact on the number of road traffic casualties. Urban population, population density and number of car drivers are positively correlated with the number of road traffic casualties. The resident population and GDP are negatively correlated with the number of road traffic casualties. According to the research conclusions, this paper puts forward some suggestions, such as optimizing the management technology of road traffic construction, strengthening the education system of traffic safety awareness of residents in the new urbanization, increasing the investment in traffic construction, and establishing an institution for differentiated training and management of drivers based on driving experience.

Security analysis of vehicle violation alarm based on intelligent monitoring platform

With the development and progress of the society, travel modes are constantly diversified. Passenger transport is deeply loved by people for its advantages of efficiency, convenience and comfort. Traffic accidents caused by overspeed driving and fatigue driving are still a big problem to be solved. This paper will be combined with intelligent monitoring platform to provide vehicle violation alarm record, through the analysis of the statistical data analysis and interview method driver speeding, fatigue driving, and combined with the enterprise management department, to improve the relevant management regulations and put forward new measures, effectively reduce the occurrence of vehicle violations, so as to reduce the occurrence of traffic accidents.

Forecasting of Traffic Accident Occurrence Pattern Using LSTM

Forecasting of Traffic Accident Occurrence Pattern Using LSTM

Self-perceived sleep during the Maintenance of Wakefulness Test: how does it predict accidental risk in patients with sleep disorders?

To determine whether the feeling of having slept or not during the Maintenance of Wakefulness Test (MWT) is associated with the occurrence of self-reported sleep-related traffic near misses and accidents in patients with sleep disorders. This study was conducted in patients hospitalized in a French sleep center to perform a 4 × 40 min MWT. Relationship between mean sleep latency on the MWT, feeling of having slept or not during MWT trials and sleep-related near misses and accidents reported during the past year was analyzed. One hundred and ninety-two patients suffering from OSAS, idiopathic hypersomnia, narcolepsy, restless leg syndrome or insufficient sleep syndrome were included. One hundred and sixty-five patients presented no or one misjudgment of feeling of having slept during MWT trials while 27 presented more than two misjudgments. Almost half of the latter (48.1%) reported a sleepiness-related traffic near miss or accident in the past year versus only one third (27.9%) for the former (p < 0.05). Multivariate logistic regression showed that patients with more than two misjudgments had a 2.52-fold (95% CI, 1.07-5.95, p < 0.05) increase in the risk of reporting a sleepiness-related near miss/accident. Misjudgment in self-perceived sleep during the MWT is associated with the occurrence of self-reported sleepiness-related traffic near misses and accidents in the past year in patients suffering from sleep disorders. Asking about the perception of the occurrence of sleep during the MWT could be used to improve driving risk assessment in addition to sleep latencies.

Road Surface State Recognition Based on Semantic Segmentation

In extreme bad weather conditions, vehicles can timely adjust the speed and distance according to the road information, which will effectively reduce the occurrence of traffic accidents. Road type and road adhesion coefficient are the main factors affecting road condition. The traditional identification method of road adhesion coefficient has the characteristics of high cost and low reliability. At the same time, the road surface information recognition technology based on machine vision has become a hot spot in the current research, but the low accuracy and poor robustness are always the difficulties in the research. Based on the semantic segmentation model which is popular in machine vision, a new pavement type recognition technology is proposed by improving the model output network. According to the existing literature, vehicle road surface can be divided into the following 9 categories, including wet asphalt road, dry asphalt road, wet concrete road, dry concrete road, wet soil road, dry soil road, gravel road, compacting snow road and icy road. The standard data set is made by collecting road pictures through taking photos, downloading and other ways, and the pre-processed data set is used to train the improved semantic segmentation network model. Through many tests, training parameters that achieve the desired effect are selected for model parameter solidification, and the semantic segmentation model after solidification is used to predict the road image acquired by the camera. According to the prediction results of the model, the categories of the current driving road surface are obtained. The test results of a large number of road images show that the average classification accuracy of the nine road types is about 94%, which effectively improves the identification accuracy and robustness of the current road types. At the same time, the predicted time of a single frame image on a specific test platform is about 0.028 6 s, which meets the real-time requirements.

What Affects the E-Bicycle Speed Perception in the Era of Eco-Sustainable Mobility: A Driving Simulator Study

The increase in the number of electric bicycles worldwide has resulted in a rise in the number of traffic accidents involving e-bicyclists. Previous studies have been based on analyzing the use, advantages and disadvantages of e-bicycles, whereas only a small number of studies have been focused on analyzing the e-bicycle traffic safety, particularly the factors leading to the occurrence of traffic accidents. One of the factors affecting the occurrence of traffic accidents is the incorrect perception of the e-bicycle speed by other traffic participants. To examine the mentioned problem, the authors of this paper conducted an experimental study to determine what affects the e-bicycle speed perception. The experiment included 175 participants, aged 18 to 50. The research was conducted under laboratory conditions using a driving simulator, at different e-bicycle speeds (10 km/h, 20 km/h and 30 km/h), in the situations in which the e-bicyclist was (not) using a reflective vest. The results show statistically significant differences in the e-bicycle speed perception when the e-bicyclist does not use/uses a reflective vest. Besides, the driving licence categories of traffic participants and their driving experience also have a significant impact on the perception of the e-bicycle speed.

Research on Urban Road Traffic Accident Characteristics and Countermeasures: A Case Study of Ningbo City

Current road traffic safety situation is still challenging globally and needs continuous public attention. Exploring the characteristics of road traffic accidents could improve the understanding of accident causation mechanism and help develop countermeasures to reduce the accident occurrences. The paper first explores the general characteristics of urban road traffic accidents from the perspectives of temporal, spatial, and personal distributions. Random Forest (RF) Model is then utilized to identify important features affecting the occurrence of traffic accidents. Accordant countermeasures are finally discussed and proposed.

Application of New Generation Artificial Intelligence in Traffic Informatization

With the rapid development of economy, continuous improvement of urbanization level, increasing number of private cars and increasingly prominent urban traffic problems, intelligent transportation has become a research hotspot at home and abroad. As a new generation of high and new technology, artificial intelligence technology plays a decisive role in the development of China’s transportation. On the basis of studying the level of China’s traffic informatization, this paper uses the YOLO algorithm model and selects the CCTSDB data set to study the traffic sign recognition in traffic informatization, and verifies the accuracy of the algorithm through experimental results, and changes the traditional driving mode, improve the level of informatization, and reduce the occurrence of traffic accidents.

Application Strategy of Big Data Processing Technology in Intelligent Transportation

With the rapid development of China’s society and economy, the process of urbanization has been accelerated, and the transportation system has become more complicated, especially the frequent occurrence of traffic accidents, traffic congestions, and environmental pollution. In the context of the rapid development of Internet technology, digital technology, artificial intelligence technology, etc. We apply them to traffic management as effective ways to improve China’s traffic operation management. Based on big data processing technology, this paper discusses its application strategy in intelligent transportation, in hope of serving as a reference.

The impact of driving schools' training on fatal traffic accidents in the Slovak Republic

This paper deals with fatal traffic accidents in the period 2017-2019, caused by drivers who have held driving licenses for less than five years. Specifically, it examines an interconnection between these accidents and the driving schools being completed by these drivers. Furthermore, it analyses whether the perpetrators of traffic accidents with short driving experience are graduates of the same driving schools, and thus, whether the occurrence of serious traffic accidents is directly related to the quality of training in specific driving schools.

Research on the Method of Traffic Signal Image Detection and Recognition

Traffic lights play an important role in the distribution of conflicting traffic flow, and the correct detection and identification of traffic lights play an important role in reducing the occurrence of traffic accidents. Since the 1980s, China has begun the research and development of intelligent self-driving vehicles, which have higher requirements for all kinds of traffic sign recognition. Based on the above situation, this paper has done some research on traffic light image detection and recognition, which has far-reaching significance in the field of auxiliary driving and self-driving. The technical scheme adopted in this paper first uses the initial segmentation of RGB space and HSV color segmentation to get the selection of traffic lights, and then uses the lightweight network model to identify traffic lights and finally get the information of traffic lights. The experimental results show that the recognition accuracy and speed of this scheme can meet the requirements of the actual driving process.

Non-motorized Vehicle Traffic Accidents in China: Analysing Road Users’ Precrash Behaviors and Implications for Road Safety

The paper aims to explore underlying patterns of non-motorized vehicle (NM, including both regular bicycles and e-bikes) traffic accident occurrences based on precrash behaviors. A quarter-year data of NM accidents was collected by Yinzhou Traffic Police Department of Ningbo, China. Descriptive statistics and Rough Set theory were used to examine rules within different types of NM accidents from temporal, spatial, and behavioral aspects. Some main findings include: behavior patterns of different parties involved vary across different accident types, levels of roads, and intersections; motorized vehicle’s illegal turning as well as NM’s reverse riding are the two key behaviors that deserve concern across all levels of roads and intersection; in addition, for higher level urban roads more attention should be focused on lane violations of motorized vehicles, and for branch roads and intersections prevention efforts could be directed to motorized vehicles’ illegal turning around and NM’s red-light running respectively. Results from this paper could facilitate related staff formulating more targeted policies to make roadways safer.

An Assessment of the Climatic Characteristics and Their Impact on the Traffic Accidents in the City of Ramadi

The facets of the climate have a great impact on the traffic of different means of transportation as long as bad weather conditions usually reduce the density of the transportation, especially on the land roads, while good conditions help to increase the use of the land roads.&#x0D; The climatic characteristics have a direct impact on the occurrence of the traffic accidents for the increase in the amount of rain, the fog and the intensity of fog are all responsible factors for impeding traffic stream. This study aims at identifying the effect of the climatic characteristics on the traffic accidents in the Ramadi city through the climatic data and trying to find out the impact of each element on climatic ones on the traffic stream in the study area.&#x0D; This study also aims at reducing the impact of the climate in the traffic accidents in Ramadi city via finding some treatments to reduce the impact of the climatic characteristics in the occurrence of traffic accidents.&#x0D; The study comes up with that all the climate elements: rain, dust storms and fog have a direct effect on the occurrence of a number of accidents in the study area.

Psychoactive Substance Use and Its Associated Factors among Truck Drivers in Ethiopia.

Background Road traffic accidents (RTAs) remain an important public health issue worldwide. Psychoactive substance use is one of the main contributors to the occurrence of traffic accidents, and its use by truck drivers is a global problem. Also, psychoactive substance use is a commonly observed behavior among truck drivers. To the best of our knowledge, no evidence shows the prevalence and factors associated with psychoactive substance use among truck drivers in Ethiopia. Therefore, this study was aimed at assessing the prevalence and factors associated with psychoactive substance use among truck drivers in Ethiopia. Methods A cross-sectional study was conducted among 400 systematically selected truck drivers at Modjo dry port in Ethiopia, from February 1 to March 1, 2018. The data were collected through face-to-face individual interviews using a structured questionnaire. The collected data were entered into EpiData version 4.2.0.0 and analyzed using SPSS version 20. Binary logistic regression analysis was computed to determine the association using crude and adjusted odds ratios at 95% confidence intervals. The level of significance was declared at p value < 0.05 in the multivariable binary logistic regression analysis. Results Of the 400 truck drivers interviewed, the overall one-month self-reported prevalence of psychoactive substance use was 70% (n = 280). In the multivariable binary logistic regression analysis, aged 38 years and above (AOR = 0.40, 95% CI [0.23-0.69]), Christianity religion (AOR = 0.52, 95% CI [0.28-0.97]), college and university education (AOR = 3.47, 95% CI [1.27-9.47]), having a family size of 3 or more (AOR = 0.34, 95% CI [0.20-0.60]), having 6 or more hours spent sleeping at night (AOR = 0.46, 95% CI [0.28-0.75]), and rest breaks between driving (AOR = 2.13, 95% CI [1.14-3.97]) were significantly associated with psychoactive substance use. Conclusion The one-month prevalence of psychoactive substance use among truck drivers was remarkably high. We can conclude that psychoactive substance use is a public health problem among truck drivers, which is a major threat to themselves and others on the road. The sociodemographic and occupational factors are the factors associated with drivers' psychoactive substance use. Therefore, devising health education and counseling program for drivers to tackle the problem plays paramount importance.

Investigation of Traffic Accident Hotspots in Yazd City using GIS

INTRODUCTION: The importance of safety and prevention of traffic accidents have highlighted the necessity of research and investigation in this field. The present study aimed to identify and eliminate the risk factors underlying the occurrence of inner-city traffic accidents. In so doing, scientific and effective solutions can be provided at the lowest cost to implement the slogan of "prevention is better than cure". METHODS: In this study, the accident rate-severity index method was used to identify traffic accident hotspots. The spatial units in which this index (I) was more than twice the average of the total spatial units were identified as Hot Zones FINDINGS: In the present study, the study area (6,600 meters) was divided into 66 100-meter spatial units. Using the accident rate-severity method, 6 (600 m) and 19 (1900 m) spatial units were identified as Hot Zone (9.1%) and Yellow Zone (28.8%), respectively. Finally, 41 Cold Zone spatial units (62.1%) were identified (4,100 m). CONCLUSION: As evidenced by the obtained results, the following measures can be effective in the reduction of road accidents: retrospection of traffic signs in terms of number, size, location, height, installation of speed bumps in the Hot Zone and Yellow Zone spatial units, timely pruning of trees along the road-construction of underpasses and overpasses in some important points of the route (accident hotspots and Yellow Zone). Furthermore, the development of a comprehensive and long-term educational program to improve traffic safety culture in kindergartens and schools can be effective in reducing road accidents.

SOME TRAFFIC SAFETY ISSUES AT UNREGULATED PEDESTRIAN CROSSINGS IN THE CITY

The groups of participants of the pedestrian and automobile flows, whose actions cause the greatest danger to the occurrence of conflict situations in the zone of unregulated transition, are identified. The factors determining the likelihood of a traffic accident at an unregulated transition are systematized, for which probability estimates of the occurrence of road traffic accidents are calculated. As an estimated parameter, the hazard coefficient of a conflict point of an unregulated transition is proposed, which is determined by the ratio of the probability of a traffic accident in the real-time hourly interval to the average annual probability of a traffic accident reduced to the hourly interval. The dependences of the hazard ratio of an unregulated transition are established on the most significant factors: the speed mode of transport in the area before the transition and the state of the road surface.

Technology and Quality of Road Construction Projects in Kenya: A focus on Contractor’s Exposure to Technology in Machakos Sub-County

Road traffic accidents are a menace to a developing country such as Kenya. The National Transport and Safety Authority website records over 57,171 cases of road traffic accidents NTSA [15]. Adopting the root cause analysis technique, it is crucial to determine and to solve the cause of a problem in order to have a permanent solution for it. This study focuses on road construction projects which are the actual onset of road traffic accidents. Occurrences of road traffic accidents are dependent on various factors ranging from the ideology of road construction projects to the operation of a sound or unsound vehicle on the road. Numerous studies have been done to research on the factors influencing quality of road construction projects but little has been done to determine how contractors related factors affect quality of road construction projects. Due to the research gap in the pool of knowledge, this study is aimed at identifying and analyzing how contractor’s exposure to technology influences the quality of road construction projects. The study area that was analyzed in this project was Machakos sub-county situated in the Lower Eastern region of Kenya. The study was guided by the objective: to establish influence of contractor’s exposure to technology on quality of road construction projects. This study applied Systems theory approach. A descriptive survey research design and a cross-sectional approach was used to collect quantitative data by use of close-ended questionnaires. The total target population was 223and a calculative formula adopted from Kothari [8] was used to acquire a sample size of 141 respondents. Stratified random sampling technique was applied on the sample size of 141 respondents to classify them into homogenous groups. Questionnaires were used as the main data collection instruments and were issued to the respondents. The gathered data was analyzed using descriptive and inferential statistics with the data being presented descriptively using frequencies and percentages. Pearson’s product moment correlation procedure method was used to analyze the inferential statistic data and the results presented. In this study Spearman’s was used to measure the degree of association between the independent and dependent variables. On the influence of contractor’s exposure to technology, a composite mean of 3.74, standard deviation of 1.028 and a strong correlation value of 0.373 were achieved. The survey elicit conclusion was that quality of road construction projects in Machakos Sub-County was influenced by the independent variable.

Motor Vehicle Injury and Kidney Damage: Trauma, Rhabdomyolysis, Acute Kidney Injury and Beyond.

Clinical Background: Automobile accidents constitute one of the main causes of morbidity and mortality worldwide. Kidney injury related to automobile accidents occurs through several mechanisms. Among the mechanisms are direct traumatic injury, rhabdomyolysis, acute renal failure, hypovolemic shock, and others. Kidney injuries after traffic accidents can have different forms of clinical presentation, which can include morphological and functional changes of the organ. Epidemiology: Annually, approximately 1.35 million individuals are fatal victims of motor vehicle accidents. Challenges: It is of the utmost importance to recognize and adequately manage kidney injuries secondary to automobile events, since their timely identification can prevent permanent damage to the organ and, at times, save lives. Prevention and Treatment: An approach with a focus on traffic safety is ideal for all users. The cornerstones of this approach are safe roads and road shoulders, adequate speeds, safe vehicles, and trained drivers. Kidney injury and death due to this condition can be prevented by effective and integrated traffic legislation. After the occurrence of traffic accidents, effective emergency care is vital. Thus, a series of urgent actions are essential to provide assistance to the injured individual. In patients who are victims of car accidents and develop acute kidney injury and rhabdomyolysis, a timely diagnosis is extremely important to be able to offer assistance to the patient still in the early stages.