Analysis Of Traffic Accidents Research Articles

Analysis of traffic accidents based on Spark and causal inference

Traffic accidents have occurred frequently in recent years, causing great losses to personal and property safety. Studying traffic accident data is helpful to identify key factors of traffic accidents from big data. This paper processes and calculates big data based on the Spark platform. By introducing causal inference into the analysis of traffic accidents, it establishes the causal relationships between 17 factors and the severity of traffic accidents, thereby analyzing the root causes and intermediate causes of the accidents. In addition, this paper also conducts an intervention study to evaluate the influence weight of each factor. The study finds that the physical conditions of pedestrians and weather conditions are inferred to be the root causes, and the others are intermediate causes. Besides that, the presence of police force and reduced traffic volume are considered to be the best ways to reduce traffic accidents. Therefore, this article believes that in real life, we should reduce the incidence of traffic accidents by controlling traffic flow and increasing patrol or on-duty police force. These findings provide a scientific basis for traffic management departments to develop more effective traffic safety strategies.

Above the roads: Unleashing unmanned aerial vehicles and image processing for traffic analysis

Road traffic surveillance using unmanned aerial vehicles is a practice that can be found especially in the field of intelligent vehicle management, which is still in the early stages of research and application. This paper presents three methods of analyzing traffic data. One method is a conventional one, based on Doppler radar detection and the other two methods analyze images captured by unmanned aerial vehicles, being based on deep learning techniques. After acquiring the images, they went through a complex processing process to eliminate noise and improve the clarity of the image, then the identification of the vehicles was done by recognizing moving objects and highlighting them either through a bounding box or through labelling. The quality of images obtained from unmanned aerial vehicles is similar to the quality of images obtained from fixed surveillance cameras. The comparative analysis of the results obtained through image processing, together with those obtained through a conventional method of traffic analysis, the Doppler radar, highlighted the fact that video detection used in intelligent vehicle management is a method that both researchers and local authorities can rely on the performance of traffic studies or the analysis of traffic incidents and accidents.

Geographical analysis of the impact of road networks on traffic accidents in Pishdar District

An increase in the population and its needs, the development of transportation means and the diversification of transportation networks, as well as the differences in the geographical location of the population, are reasons for paying more attention to the road and transportation sector. In addition to all this, the increase in traffic accidents at the local and global levels is another reason for the attention of different fields. Pishdar district, as a geographical unit in the Kurdistan Region, has seen significant progress in the field of transportation in recent years, which has caused it to have its share of traffic accidents. The main goal of this study is to identify the characteristics of road networks in Pishdar district as well as a geographical analysis of traffic accidents and determine the level of solving these problems that are observed in the district in a scientific way. The research plan is divided into three main parts, each of which consists of two main sections: Part 1. This part describes the characteristics of transport networks and consists of two main sections. The first section includes the classification of road networks, and the second section includes the distribution of road networks at the level of administrative units in the study area. Part 2. This part is dedicated to the role of geographical principles in the time and place of traffic accidents in Pishdar district. The first is about the difference in time according to (month and year), and the second is the difference in location according to (city and outside the city). Part 3. This part is dedicated to how the most significant natural factors, which also include the most significant human factors, affect traffic accidents.

From virtually extinct to superabundant in 35 years: establishment, population growth and shifts in management focus of the Swedish wild boar (Sus scrofa) population

BackgroundThe wild boar (Sus scrofa) was extinct in Sweden when a few animals established in the 1970s. Over the past 35 years, the species has made a substantial comeback. In this paper, we analyse wild boar population growth using three indices of population size. We also map the legislative decisions and research prompted by the expanding population. We discuss to what extent, in the eyes of the state, the view of wild boar and the management focus has shifted over time, from a perceived pest (eradication) to scarce (conservation), overabundant (reduction/control) or somewhere in between (sustainable management).ResultsWild boar harvest started in the early 1990s with a few hundred animals annually and peaked at 161,000 in 2020/2021. The distribution now comprises most of southern Sweden. Analyses of harvest and traffic accidents involving wild boar showed that the population grew exponentially until 2010/2011, after which the increase levelled off. Thus, logistic growth models showed the best fit for the full study period. We recorded 38 legislative decisions or commissions to government agencies regarding wild boar. The first decision in 1981 was to eradicate the free-ranging population. In 1987 however, the parliament decided that wild boar is native to Sweden and should be allowed in restricted extent. Later decisions mainly concerned hunting regulations and hunting methods as direct means to increase harvest and regulate the population. Another topic, increasing in importance over time, was to facilitate the use of wild boar meat to indirectly stimulate harvest. A local outbreak of African swine fever in 2023 necessitated a stamping out strategy in the affected area. We found 44 scientific papers regarding the present free-ranging population. Topics include movements and feeding patterns, hunting, reproduction, and population development.ConclusionsThe state historically regarded wild boar as a pest to be eradicated. This changed with the decision that wild boar should be allowed in restricted extent, suggesting a conservation approach. In response to population growth, the focus shifted to means facilitating sustainable management and, lately, reducing growth. The story of wild boar in Sweden illustrates attempts to mitigate conflicts and balance interests in wildlife management.

The importance of road safety inspection for the improvement of pedestrian safety in traffic

Road safety inspection is a modern tool for improving road safety, that is, a preventive tool that is applied on roads in use. The goal of preventive action in road safety is to improve road safety, through reducing the number and consequences of traffic accidents. The road safety inspection involves the analysis of general data about the section, the analysis of traffic accidents, but also the analysis of specific road defects. 11 specific elements are analyzed, including vulnerable road users, including pedestrians, cyclists and motorcyclists. Vulnerable road users are particularly at risk because in the event of a traffic accident, they suffer more severe consequences because their body is not physically protected. This paper shows the importance of road safety inspections for the improvement of pedestrian safety in traffic.

Statistical Analysis of Traffic Accidents Involving Elderly Drivers

Statistical Analysis of Traffic Accidents Involving Elderly Drivers

A Study on the Feng Shui Characteristics of “Pedestrian Accident Hotspots” in Seoul (Focusing on traffic accident analysis system data)

A Study on the Feng Shui Characteristics of “Pedestrian Accident Hotspots” in Seoul (Focusing on traffic accident analysis system data)

Research on Drivers' Behaviors of Heavy Goods Vehicles Based on Traffic Accident Analysis

Heavy goods vehicles (HGVs) have played a vital role in transporting goods. These goods are closely related to social development and people's lives, including daily necessities and parcels, and even industrial raw materials such as coal and steel. However, for many reasons, the number and incidence of HGVs traffic accidents remain high in all countries. Since HGVs have the characteristics of large volume, large mass and fast speed, the consequences of HGVs traffic accidents are often very serious, causing numerous property damages, and even seriously threatening people's lives. There are many factors that contribute to this tragedy, but it is worth noticing that HGVs drivers played a vital role. Their bad driving behaviors and habits may be to blame for the accidents. This paper identifies the bad driving behaviors and habits of HGVs drivers that may lead to traffic accidents, and analyzes the impact of these behaviors. This paper also attempts to establish the relationship between external factors such as insurance and drivers' driving behaviors, and explores the impact of public policies on traffic accidents. Finally, this paper draws the conclusion that many bad driving behaviors will lead to HGVs accidents, and several actions from drivers, government and companies can be taken to improve traffic safety. This paper adopts the field observation method, the interview survey method and the literature research method.

Applying a two-step cluster algorithm in traffic accident data analysis

Cluster analysis is often employed as the initial stage in organizing heterogeneous data into homogeneous groups. Choosing an effective clustering approach and an ideal number of clusters in a traffic accident dataset might be complex and challenging. This study aims to evaluate the effectiveness of k-means and two-step cluster methods. Subsequently, the two-step cluster method and GIS are applied to analyze the traffic accident datasets from 2015 to 2017 in Hanoi, Vietnam. First, according to the Silhouette score, the two-step cluster method achieved a higher score of 0.563, while the k-means method scored 0.341. A higher Silhouette score indicates more well-defined clusters. Second, the research suggests combining the two-step cluster method with GIS for analyzing traffic accident datasets. The outcome identifies five typical types of accidents in Hanoi. In addition, the locations of various accident types were visually illustrated on a map, enabling traffic officials to recommend precise and urgent countermeasures. Importantly, the clustering results reveal that the two-step cluster method exhibits a significantly higher rate of homogeneous data in the clusters compared to the k-means method. This study demonstrates that the two-step cluster method is not only more effective than the k-means method in terms of clustering ability but also in data pre-processing. The study's results enable authorities to gain a more detailed understanding of typical traffic accident patterns in Hanoi. Besides, the employed methods could potentially be applied to other regions, providing an additional avenue for analysis

Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System

Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies.

Classification of traffic accidents’ factors using TrafficRiskClassifier

The TrafficRiskClassifier model introduced in this study adopts an innovative approach that incorporates migration learning, image classification, and self-supervised learning, aiming to significantly improve the accuracy and efficiency of traffic accident risk analysis. Compared with traditional traffic safety analysis techniques, this model focuses on utilizing contextual information and situational data of traffic accidents to achieve a higher level of risk classification accuracy. The core of this approach is to deeply mine and analyze the detailed information in the accident environment, to provide more scientific and effective support for traffic accident risk prevention and response. Initially, by integrating migration learning with image classification techniques, the model proficiently extracts pivotal features from intricate traffic scenarios and formulates initial assessments of accident risks. Subsequently, self-supervised learning is incorporated in this study, augmenting the model's capability to comprehend and categorize accident imagery. The TrafficRiskClassifier model exhibits a generalization ability of 91.82%, 85.16%, and 80.92% on individual classification tasks, respectively, signifying its robust learning capacity and proficiency in managing unseen data. Furthermore, the TrafficRiskClassifier model delineates a functional nexus between accident risk and variables such as weather, road conditions, and personal factors, employing a polynomial regression approach. This methodology not only amplifies the predictive precision of the model but also renders it versatile across diverse scenarios. Through the analysis of various polynomial functions, the model achieves enhanced accuracy in classifying disparate risk levels. The outcomes demonstrate that the TrafficRiskClassifier model can efficaciously amalgamate contextual information within traffic scenarios, thereby achieving more precise classification of traffic accident risks, and consequently serving as an invaluable instrument for urban traffic safety management.

Analysis of road traffic accidents and casualties associated with electric bikes and bicycles in Guangzhou, China: A retrospective descriptive analysis

IntroductionElectric bicycles (e-bikes) and bicycles in large Chinese cities have recently witnessed substantial growth in ridership. According to related accident trends, this study analyzed characteristics and spatial distribution in the period when e-bike-related accidents rapidly increased to propose priority measures to reduce accident casualties. MethodsFor e-bike- and bicycle-related accident data from the Guangzhou Public Security Traffic Management Integrated System, linear regression was used to examine the trends in the number of accidents and age-adjusted road traffic casualties from 2011 to 2021. Then, for the period when e-bike-related accidents rapidly increased, descriptive statistics were computed regarding rider characteristics, illegal behaviors, road types, collision objects and their accident liability. One-way analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test. P < 0.05 was considered statistically significant. Finally, the density distribution of accidents was presented, and Moran's I (MI) was used for assessing spatial autocorrelation. Hotspots were identified based on an optimized hotspot analysis tool. ResultsBetween 2011 and 2021, the number of accidents and casualty rate (per 100,000 population) increased for e-bikes but decreased for bicycles. After 2018, e-bike-related accidents increased rapidly, and bicycle-related accidents plateaued. Accident hotspots were concentrated in central city areas and suburban areas close to the former. Three-quarters of accidents occurred in motorized vehicle lanes. Most occurred on roads without physically segregated nonmotorized vehicle lanes. More than three-fifths of the accidents involved motor vehicles with at least four wheels. The prevalence (per 100 people) of casualties among e-bike rider victims and cyclist victims accounted for 92.0 % and 96.5 %, respectively. A total of 71.6 % of e-bike-related accidents involved migrant workers. Riding in motorized vehicle lanes was the most common illegal behavior. ConclusionsAlthough e-bike-related and bicycle-related accidents presented similar characteristics, the sharp increase in e-bike-related accidents requires attention. To improve e-bike safety, governments should develop appropriate countermeasures to prevent riders from riding on motorways, such as improving road infrastructure, adjusting the driver's license system and addressing priority control areas.

Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)

Abstract The objective of this study is to analyse the spatial-temporal patterns of traffic accidents using data from 2011 to 2020 for the AP Vojvodina. The spatial-temporal patterns of traffic accident hotspots were identified at the municipality level using the Getis–Ord G i ⁎ {G}_{i}^{\ast } statistic in ArcGIS Pro software. Trends in traffic accidents were analysed using the Mann-Kendall (MK) statistic. A certain trend in the number of traffic accidents, fatalities, and injuries was detected in 15 out of 45 municipalities. In two municipalities, the trend in the number of traffic accidents is decreasing, while in seven, it is increasing. In three municipalities, a decreasing trend in the number of traffic fatalities was observed. A decreasing trend in traffic injuries was identified in five municipalities. Hotspot analysis on the number of traffic accidents and traffic injuries revealed hotspots in seven municipalities, while no hotspots were detected in the number of traffic fatalities. In the traffic casualties weighted number, persons in tractors, hotspots were identified in two municipalities. This research has the potential to enhance traffic safety by directing targeted safety measures towards identified hotspots. Such measures could ultimately lead to a reduction in traffic accidents, consequently curbing the number of both fatalities and injuries.

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.

Traffic modeling and accidental data analysis using GIS: A Review

Nowadays, congestion and accidents are creating major risks to cities, including delays, higher fuel usage, and compromised safety. Effective traffic modelling and accident data analysis are critical for identifying high risk identifying accident-prone locations, understanding the causes of accidents and creating focused actions to enhance traffic flow and safety. GIS is an effective tool for integrating, analysing and visualizing different geographical data relevant to transportation networks such as, traffic flow, infrastructure, and safety. It enables geographical analysis and visualization of accident hotspots by integrating accident data, road conditions, traffic numbers, and environmental factors. The use of GIS in traffic modelling and accident data analysis provides considerable benefits in urban transportation planning and management. The aim of the paper is to provide an overview of the application of GIS in traffic modelling and accidental data analysis, highlighting the methodologies, advancements, and challenges in this field. The review shall provide a comprehensive assessment of the current state of traffic modelling and accidental data analysis using GIS. It will highlight the significant contributions of GIS technology, identify key research gaps, and offer insights into future directions for enhancing transportation planning and decision-making processes.

Application of Big Data Analysis of Traffic Accidents and Violation Reports for Improving Traffic Safety

Application of Big Data Analysis of Traffic Accidents and Violation Reports for Improving Traffic Safety

Assessing the impact and implications of the revised Act on the Aggravated Punishment of Specific Crimes in preventing child traffic injuries in school zones in Korea: an interrupted time series analysis.

In 2019, a child's death in Korea led to legislation that imposed stricter penalties for school zone traffic violations. We assessed the impact of that legislation using 2017-2022 Traffic Accident Analysis System data. Adjusted analyses revealed a significant decline in severe injuries in school zones, decreasing from 11 cases to 8 cases per month (p=0.017). The legislation correlated with a reduced risk of all child traffic injuries (risk ratio, 0.987; 95% confidence interval, 0.977 to 0.997; p=0.002), indicating its efficacy in curbing accidents.

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.

Fine-Grained Accident Detection: Database and Algorithm.

This paper presents a novel fine-grained task for traffic accident analysis. Accident detection in surveillance or dashcam videos is a common task in the field of traffic accident analysis by using videos. However, common accident detection does not analyze the specific particulars of the accident, only identifies the accident's existence or occurrence time in a video. In this paper, we define the novel fine-grained accident detection task which contains fine-grained accident classification, temporal-spatial occurrence region localization, and accident severity estimation. A transformer-based framework combining the RGB and optical flow information of videos is proposed for fine-grained accident detection. Additionally, we introduce a challenging Fine-grained Accident Detection (FAD) database that covers multiple tasks in surveillance videos which places more emphasis on the overall perspective. Experimental results demonstrate that our model could effectively extract the video features for multiple tasks, indicating that current traffic accident analysis has limitations in dealing with the FAD task and that further research is indeed needed.

ВЛИЯНИЕ ВОДИТЕЛЬСКОГО СТАЖА НА АВАРИЙНОСТЬ

The analysis of traffic accidents and driver training in different countries is considered. The main causes of traffic accidents are analyzed and governments’ measures to improve road safety are considered. Various driver training programs in different countries are considered and their effectiveness is revealed