Occurrence Of Traffic Accidents Research Articles

Computer Vision-Based Lane Detection and Detection of Vehicle, Traffic Sign, Pedestrian Using YOLOv5

There has been a global increase in the number of vehicles in use, resulting in a higher occurrence of traffic accidents. Advancements in computer vision and deep learning enable vehicles to independently perceive and navigate their environment, making decisions that enhance road safety and reduce traffic accidents. Worldwide accidents can be prevented in both driver-operated and autonomous vehicles by detecting living and inanimate objects such as vehicles, pedestrians, animals, and traffic signs in the environment, as well as identifying lanes and obstacles. In our proposed system, road images are captured using a camera positioned behind the front windshield of the vehicle. Computer vision techniques are employed to detect straight or curved lanes in the captured images. The right and left lanes within the driving area of the vehicle are identified, and the drivable area of the vehicle is highlighted with a different color. To detect traffic signs, pedestrians, cars, and bicycles around the vehicle, we utilize the YOLOv5 model, which is based on Convolutional Neural Networks. We use a combination of study-specific images and the GRAZ dataset in our research. In the object detection study, which involves 10 different objects, we evaluate the performance of five different versions of the YOLOv5 model. Our evaluation metrics include precision, recall, precision-recall curves, F1 score, and mean average precision. The experimental results clearly demonstrate the effectiveness of our proposed lane detection and object detection method.

The effect of guardrail color on driver behavior based on driving style along mountain curves

Objective Mountain highways are linearly complex, with extensive curves and high accident injury rates, how to improve driving safety is the key to traffic safety management on mountain highways, and it also meets the need for harmonious and sustainable development of the society. Therefore, this study investigates the effects of different guardrail color configurations on the driving behavior of different styles of drivers when driving on mountainous curves from the perspective of improving road aids – guardrails. Methods A virtual reality experiment was designed using a driving simulator and VR technology, and 64 subjects were recruited to participate and complete the experiment. Results Drivers with non-adaptive driving styles (Reckless, Angry, Anxious) traveled at significantly higher speeds than subjects with adaptive driving styles (Cautious) on mountainous roads; drivers with Cautious styles had better lane-keeping ability when passing through different radii of curves as compared to non-adaptive drivers; and the red and yellow guardrails were more effective in decreasing the speeds at which drivers passed and in increasing the stability of lane-keeping. Conclusions The results of the study show that the effectiveness of red and yellow guardrails is better, which provides a reference for the traffic management department to propose a standardized color setting of guardrails in mountainous areas, which is conducive to the development of more precise traffic management measures to reduce the occurrence of traffic accidents.

Review of Road Traffic Accident Burdens in Transportation Sector of Ethiopia

The transportation sector has a very immense role in the economic development of a given country. Because human beings' day-to-day activities re highly depends on the transport service. But this sector has its own drawbacks, like road traffic crashes and environmental pollution. In Ethiopia, the road transport sector is poor in terms of quality and service delivery systems. Due to this, the frequent occurrence of road traffic crashes has been recorded in the country continuously. To that end, this paper was mainly intended to review road traffic accident burdens in the transportation sector of Ethiopia. In detail, reviewing the costs of road traffic accidents, the causing factors of road traffic accidents, the types of vehicles highly involved in road traffic accidents, and the victim groups of communities affected by road traffic accidents at the national level were the specific objectives of this paper. Meanwhile, all road traffic accident data found in Ethiopia from the federal police commission report papers, journal articles, and dissertation papers were used for analysis purposes. Besides, the collected data is presented in tables and figures. Furthermore, the data was analyzed and interpreted based on a descriptive research design. Finally, the result of this review shows that trends of road traffic accidents in Ethiopia have been increasing over time by killing, injuring people and damaging resources. Drivers’ bad behaviors, low know-how of road users, poor road infrastructure, old vehicle conditions, and environmental factors are aggravating the occurrence of road traffic accidents in the country. Thus, to tackle this problem, educating people, enforcing traffic laws, enhancing engineering measures, and giving emergency responses were the recommendations given in this paper.

Perception of drivers toward road safety and factors that cause road accidents in Riyadh city of Saudi Arabia

Injury as a result of road traffic accidents (RTA) is one of the leading causes of death and disability-adjusted life years. Strategies to handle this major public health problem, which has significant consequences for the Saudi economy and families, have been underway for many years but with very little success. This study’s main aim is to determine respondents’ perceptions of traffic safety and the factors that lead to road traffic accidents. It is a cross-sectional study conducted on randomly selected university students, faculty drivers, and visitors using King Saud University’s parking facility. Data were collected from 381 drivers using a pretested questionnaire, which comprises questions intended to attain the study’s objectives. The critical factors affecting traffic safety are public information followed by road maintenance, Garage, vehicle inspection, Road design and equipment, Training of drivers, Sanctions and Police enforcement, Protective devices and vehicle design, and post-accidental care. Road traffic accidents were found to be 39.0% with respondents aged less than 20 years, followed by ages between 20–35 years, which was 34.8%, and only 2.4% above 50 years. The most frequent occurrence of road traffic accidents was angle collision (39.0%) and back collision (18.9%), followed by hitting a fixed object (17.7%). Distraction habits during driving include cell phone usage (56.7%), followed by smoking (24.4%), and eating and drinking (18.9%) while driving. The most common traffic violations were increased speed limit (48.1) and loss of driving license (11.8%). The most common injuries were head (39.0%) and limbs (22.0%). A significant association between drivers’ age during the accident and distraction habits has been found (p < 0.000). The study demonstrated that drinking/eating, cell phone use, and violation of traffic rules are significant reasons for RTA. The findings of this study will contribute to a more comprehensive knowledge of road traffic accidents for the formation of effective strategies and evidence-based policies for the prevention of accidents.

Analysis of the elderly pedestrian traffic accidents in urban scenarios: the case of the Spanish municipalities

As the elderly population grows, there is a greater concern for their safety on the roads. This is particularly important for elderly pedestrians who are more vulnerable to accidents. In Spain, one of the most aged countries in the world, the elderly accounted for 70% of all pedestrian deaths in 2019. In this study, the focus was on analysing the occurrence of elderly pedestrian-vehicle collisions in Spanish municipalities and how it is related to the built environment. The study used the hurdle negative binomial model to analyse the number of elderly and non-elderly pedestrian accidents per municipality in 2016-2019. The exploratory analysis showed that cities above 50,000 inhabitants were safer for the elderly, and larger provincial capitals had lower elderly pedestrian traffic accident rates. The occurrence of all pedestrian traffic accidents was linked to the socio-demographic features. For elderly pedestrians, land use was found to be influential, with a lower proportion of land covered by manufacturing and service activities linked to a smaller number of accidents. Results showed that improving road safety for older pedestrians may not necessarily compromise the situation for the rest of population. Hence, policymakers should focus on infrastructure improvements adapted to the needs of elderly pedestrians.

Presenteeism and Traffic Accident Among Taxi Drivers: A Prospective Cohort Study in Japan

BackgroundTraffic accidents involving professional drivers have serious societal repercussions. Unique occupational stressors and health risks exacerbate the likelihood of traffic accidents among professional drivers. This study explores the association between presenteeism—impaired work performance due to working while unwell—and traffic accident risk among professional taxi drivers in Japan. MethodsA prospective cohort study was conducted from June 2022 to February 2023, involving taxi drivers from a single company in Fukuoka Prefecture, Japan. Presenteeism was assessed using the Work Functioning Impairment Scale (WFun). Primary outcome involved the number of self-reported minor traffic accidents. The incidence rate ratio (IRR) of minor traffic accident occurrences was estimated using a Poisson regression analysis, adjusted for confounders including sex, age, and driving experience. ResultsOf 838 targeted drivers, 435 were included in the analysis. Higher baseline work functioning impairment was associated with a significant trend of increasing IRR of minor traffic accidents (p for trend = 0.045). A dose-response relationship was seen between the degree of presenteeism and incidence rate of minor traffic accidents. ConclusionHigher levels of presenteeism were associated with an increased risk of traffic accidents among taxi drivers. The findings underscore the need for socio-economic support and prioritized health management to mitigate traffic accident risk among professional drivers. This study highlights the importance of managing non-critical health issues alongside serious health conditions for safer driving practices among professional drivers in Japan.

Knowledge, Attitude, and Perception: Impact on Online Ojek Driving Safety

The high number of accidents for ojek online motorcycle users cannot be separated from the behavior of the riders. Motorist behavior factors have an important contribution in determining the occurrence of road user traffic accidents. Thus, the purpose of this study is to ascertain how knowledge, attitudes, and perceptions will affect motorcycle safety riding behavior in Jakarta in 2023 when it comes to ojek online. This study used survey method with quantitative research data. The population in this study was all ojek online motorcycle drivers in Jakarta and the sample used amounted to 100 respondents. The results of the analysis, computations, and talks demonstrate that knowledge, attitudes, and perceptions all work together to influence Jakarta ojek online motorbike riders’ safety riding behavior positively and significantly. Where partially the variable knowledge has a positive and significant influence on the safety riding behavior of ojek online motorcycle taxis in Jakarta. The same thing with attitude variables that have a positive and significant influence on ojek online motorcycle safety riding behavior in Jakarta. Similarly, the perception variable significantly and favorably affects Jakarta motorbike riders' safety-riding habits (Ojek Online).

Research on elastic governance strategy of urban public safety based on entropy weighted discrete clustering method

<p>Currently, there are problems in the governance of urban public safety, such as a single entity, outdated governance concepts, and immature governance technologies. This article combines big data analysis technology and utilizes intelligent emergency mechanisms to conduct in-depth research on governance strategies to enhance the resilience of urban public safety to disasters. This article first integrates big data analysis technologies, such as the Internet of Things and cloud computing, into UPS (urban public safety) and then builds a UPS system based on this. Combining the entropy-weighted dispersion clustering method, evaluate the values of urban public safety indicators. In order to verify the effectiveness of the intelligent emergency mechanism based on big data analysis, this article conducted experimental analysis on it. Under the intelligent emergency mechanism algorithm, the average seismic compliance rate of buildings in various cities has reached 88.57%. The conclusion indicates that an intelligent emergency mechanism based on big data analysis can enhance the adaptability of urban public safety governance strategies, improve the seismic and fire warning monitoring capabilities of urban buildings, reduce the occurrence of traffic accidents, and provide more guarantees for urban fire safety.</p>

TIME PATTERNS OF RECEIVING DAMAGE AS A RESULT OF A TRAFFIC ACCIDENT

This study is aimed at identifying the most likely intervals and temporal patterns of the occurrence of traffic accidents, which is very relevant and important in the context of the development of road safety. Understanding the specifics of the times and conditions when the likelihood of accidents increases can contribute to the development and implementation of effective road safety measures. The obtained information on the temporal trends of road accidents can become the basis for the development of accident prevention strategies, the establishment of special preventive measures or recommendations for the optimal use of roads at a certain time.
 Aim of the work. To determine the temporal patterns of the occurrence of road accidents and to identify the most likely intervals of their occurrence.
 Results. The results showed that the most likely time of day for fatal crashes is between 6.00 p.m. and 11.00 p.m., and they also occur more often on holidays and weekends, with a tendency to increase in the winter months. Thus, road accidents have clearly defined time features, in which the increased probability of occurrence is indicated.
 Conclusion. The results of this study indicate the identification of temporal patterns in the occurrence of road accidents, which can be used for the development of special measures to improve road safety.

An empirical analysis of the effect of economic activity and COVID-19 restrictions on road traffic accidents in Italy

Road traffic accidents are one of the leading causes of death for the young segment of the population. Prevention strategies can mitigate the occurrence of road accidents, and successful actions should take into account the most relevant causes of accidents. We use a comprehensive dataset which reports information from police records on all road traffic accidents with injures and/or deaths in Italy between 2014 and 2020 to explore the relationships among economic forces, driving behaviours and accident frequency and severity. More specifically, the natural experiment setting provided by the 2020 COVID-19-related movement restrictions allows to capture the effect of traffic intensity and other factors on the frequency of serious accidents and mortality. The empirical findings confirm a direct relationship between the level of economic activity and the frequency of accidents, whereas there is no clear link with their severity. Instead, lockdown periods are associated with a relatively higher occurrence of fatal road traffic accidents, due to both riskier driving behaviours at times of reduced traffic, and changes in transportation habits reflected by a higher probability of accidents involving bicycles and motorcycles.

A deep learning approach for robust traffic accident information extraction from online chinese news

AbstractRoad traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence‐wise and context‐wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon‐BiLSTM‐CRF method. The proposed approach is validated by comparing it with state‐of‐the‐art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1‐score. It improves the performance of the best benchmark model (BiLSTM‐CRF) by 18.8% in precision and 12.08% in F1‐score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice.

Location Analytics of Routine Occurrences (LARO) to Identify Locations with Regularly Occurring Events with a Case Study on Traffic Accidents

Conventional spatiotemporal methods take frequentist or density-based approaches to map event clusters over time. While these methods discern hotspots of varying continuity in space and time, their findings overlook locations of routine occurrences where the geographic context may contribute to the regularity of event occurrences. Hence, this research aims to recognize the routine occurrences of point events and relate site characteristics and situation dynamics around these locations to explain the regular occurrences. We developed an algorithm, Location Analytics of Routine Occurrences (LARO), to determine an appropriate temporal unit based on event periodicity, seek locations of routine occurrences, and geographically contextualize these locations through spatial association mining. We demonstrated LARO in a case study with over 250,000 reported traffic accidents from 2010 to 2018 in Dallas, Texas, United States. LARO identified three distinctive locations, each exhibiting varying frequencies of traffic accidents at each weekly hour. The findings indicated that locations with routine traffic accidents are surrounded by high densities of stores, restaurants, entertainment, and businesses. The timing of traffic accidents showed a strong relationship with human activities around these points of interest. Besides the LARO algorithm, this study contributes to the understanding of previously overlooked periodicity in traffic accidents, emphasizing the association between periodic human activities and the occurrence of street-level traffic accidents. The proposed LARO algorithm is applicable to occurrences of point-based events, such as crime incidents or animal sightings.

Analysis of Traffic Accidents Based on the Integration Model

To enhance the safety of road traffic operations, this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics. Initially, the process involved data cleaning, transformation, and normalization. Subsequently, various classification models were constructed, including logistic regression, k-nearest neighbors, gradient boosting, decision trees, AdaBoost, and extra trees models. Evaluation metrics such as accuracy, precision, recall, F1 score, and Hamming loss were employed. Upon analysis, the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models. Based on the model’s output results, an in-depth examination of the factors influencing traffic accidents was conducted. Additionally, measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented. These findings served as a valuable reference for mitigating the occurrence of traffic accidents.

Vehicle Trajectory Prediction Based on Local Dynamic Graph Spatiotemporal–Long Short-Term Memory Model

Traffic congestion and frequent traffic accidents have become the main problems affecting urban traffic. The effective location prediction of vehicle trajectory can help alleviate traffic congestion, reduce the occurrence of traffic accidents, and optimize the urban traffic system. Vehicle trajectory is closely related to the surrounding Point of Interest (POI). POI can be considered as the spatial feature and can be fused with trajectory points to improve prediction accuracy. A Local Dynamic Graph Spatiotemporal–Long Short-Term Memory (LDGST-LSTM) was proposed in this paper to extract and fuse the POI knowledge and realize next location prediction. POI semantic information was learned by constructing the traffic knowledge graph, and spatial and temporal features were extracted by combining the Graph Attention Network (GAT) and temporal attention mechanism. The effectiveness of LDGST-LSTM was verified on two datasets, including Chengdu taxi trajectory data in August 2014 and October 2018. The accuracy and robustness of the proposed model were significantly improved compared with the benchmark models. The effects of major components in the proposed model were also evaluated through an ablation experiment. Moreover, the weights of POI that influence location prediction were visualized to improve the interpretability of the proposed model.

Research on Early Warning Technology for High-Grade Highway Pavement Damage

Road surface damage on high-grade highways has a serious impact on driving safety. Conventional road surface damage detection mainly adopts manual detection technology. The detection efficiency is low, and the detection results are easily disturbed by human factors, which is not conducive to objective and accurate evaluation of road damage. To solve the problems of accurate collection, efficient identification, and correct positioning of high-grade road surface damage data, the research group has studied the early warning technology of road surface damage. The proposed early warning technology scheme enables drivers to take preventive driving action in a timely manner, avoids the occurrence of traffic accidents, and provides drivers with reasonable avoidance schemes to effectively prevent the occurrence of traffic accidents.

Factors associated with external causes in elderly attended by the mobile emergency care service.

To analyze the prevalence and factors associated with external causes in elderly people attended by the mobile emergency care service. Cross-sectional study with 1,972 pre-hospital care records of elderly victims of external causes from 2019 to 2020. A descriptive and bivariate analysis was performed, with a significance level of 5% (p<0.05). The prevalence of external causes in elderly people attended by the mobile emergency service was 12.2%. Falling was the most frequent occurrence. The associations of the occurrence of falls with age from 90 years old (OR=29.31; p<0.001) and female gender (OR=5.38; p<0.001) stood out, as well as the suspicion of ingestion of alcoholic beverages with occurrence of violence (OR=4.17; p<0.001) and traffic accidents (OR=1.97; p<0.001). The study showed factors associated with injuries due to external causes in theelderly and may support the formulation of coping strategies for this problem.

Pemodelan Kondisi Geometrik Jalan Terhadap Potensi Kecelakaan Lalu Lintas

Road infrastructure has an impact on the occurrence of road traffic accidents. The geometry of a road is a variable that influences the occurrence of road traffic accidents. Interpreting the observed crashes into an equation that can describe the crashes is one of the most difficult problems faced by scientists and engineers in any research. The traffic accident research conducted is still limited to analyzing the causes of accidents and jumping to handling strategies that do not reveal how much geometric influence on accidents at the location. This research was developed using a comprehensive modeling approach to illustrate how highway geometric design, in this case the alignment, influences crash rates. This study aims to analyze the influence of road geometrics on the incidence of accidents. The method used is multiple liner regression measurement model using R-Studio. The variables to be studied include road geometry planning parameters, especially horizontal alignments which include stopping sight distance (X1), radius of horizontal curve (X2), shoulder width X3, and transition curve length (X4). The results of the study obtained a relationship in the mathematical equation Y = 99.55508-0.02634X1+1.17647X2-0.06999X3-0.03906X4 with the most significant correlation value produced is the transition curve length and radius of horizontal curve. Based on the multiple R-squareed value of 0.8775 this model has a very strong correlation category. Where the ability of X1 to X4 is able to explain Y by 80%.

Factors related to safety riding behaviors among grade XII students of SMKN 3 Mandau Duri

Background: The occurrence of traffic accidents is proof that road users are undisciplined in riding. Accident rates among the students of SMKN 3 Mandau Duri reached their highest levels in 2019 and 2022. Based on the information from interviews conducted with 6 students, the causes of the accidents were that they rode without SIM C, three of them mentioned that wearing a helmet was not compulsory, and peers asked them to act unsafely. This study is aimed at finding out the correlation between knowledge, attitudes, possession of SIM C, as well as peers and safety riding behaviors among the Grade XII students of SMKN 3 Mandau Duri. Methods: This study is quantitative in nature using a cross sectional approach. The population of this study consisted of 309 students from which proportional random sampling was applied so that as many as 174 respondents were selected as the samples. The data analysis was executed using univariate and bivariate Chi Square tests. The instrument utilized in this study was the questionnaire from researchers whose validity and reliability have been tested. Results: Correlations were found between knowledge and safety riding behaviors (p-value = 0.006), attitudes and safety riding behaviors (p-value = 0.000), SIM C possession and safety riding behaviors (p-value = 0.000), and peers’ influence and safety riding behaviors (p-value = 0.000). Conclusion: There were correlations between knowledge, attitude, SIM C possession, as well as peers’ influence and safety riding behaviors among the Grade XII students of SMKN 3 Mandau Duri

Research on Traffic Accident Risk Prediction Method Based on Spatial and Visual Semantics

Predicting traffic accidents involves analyzing historical data, determining the relevant factors affecting the occurrence of traffic accidents, and predicting the likelihood of future traffic accidents. Most of the previous studies used statistical methods or single deep learning network model prediction methods while ignoring the visual effects of the city landscape on the drivers and the zero-inflation problem, resulting in poor prediction performance. Therefore, this paper constructs a city traffic accident risk prediction model that incorporates spatial and visual effects on drivers. The improved STGCN model is used in the model, a CNN and GRU replace the origin space–time convolution layer, two layers of a GCN are added to extract the city landscape similarity of different regions, and a BN layer is added to solve the gradient explosion problem. Finally, the features extracted from the time–space correlation module, the city landscape similarity module and the spatial correlation module are fused. The model is trained with the self-made Chicago dataset and compared with the existing network model. The comparison experiment proves that the prediction effect of the model in both the full time period and the high-frequency time period is better than that of the existing model. The ablation experiment proves that the city landscape similarity module added in this paper performs well in the high-frequency area.

ReMAHA–CatBoost: Addressing Imbalanced Data in Traffic Accident Prediction Tasks

Using historical information from traffic accidents to predict accidents has always been an area of active exploration by researchers in the field of transportation. However, predicting only the occurrence of traffic accidents is insufficient for providing comprehensive information to relevant authorities. Therefore, further classification of predicted traffic accidents is necessary to better identify and prevent potential hazards and the escalation of accidents. Due to the significant disparity in the occurrence rates of different severity levels of traffic accidents, data imbalance becomes a critical issue. To address the challenge of predicting extremely imbalanced traffic accident events, this paper introduces a predictive framework named ReMAHA–CatBoost. To evaluate the effectiveness of ReMAHA–CatBoost, we conducted experiments on the US–Accidents traffic accident dataset, where the class label imbalance reaches up to 91.40 times. The experimental results demonstrate that the proposed model in this paper exhibits exceptional predictive performance in the domain of imbalanced traffic accident prediction.