Analysis Of Traffic Accidents Research Articles

Driver Attention Area Extraction Method Based on Deep Network Feature Visualization

The current intelligent driving technology based on image data is being widely used. However, the analysis of traffic accidents occurred in intelligent driving vehicles shows that there is an explanatory difference between the intelligent driving system based on image data and the driver’s understanding of the target information in the image. In addition, driving behavior is the driver’s response based on the analysis of road information, which is not available in the current intelligent driving system. In order to solve this problem, our paper proposes a driver attention area extraction method based on deep network feature visualization. In our method, we construct a Driver Behavior Information Network (DBIN) to map the relation between image information and driving behavior. Then we use the Deep Network Feature Visualization method (DNFV) to determine the driver’s attention area. The experimental results show that our method can extract effective road information from a real traffic scene picture and obtain the driver’s attention area. Our method can provide a useful theoretical basis and related technology of visual perception for future intelligent driving systems, driving training and assisted driving systems.

Real-time traffic accidents post-impact prediction: Based on crowdsourcing data

Traffic accident management is a critical issue for advanced intelligent traffic management. The increasingly abundant crowdsourcing data and floating car data provide new support for improving traffic accident management. This paper investigates the methods to predict the complicated behavior of traffic flow evolution after traffic accidents using crowdsourcing data. Based on the available data source, the traffic condition is divided into four levels by congestion delay index: severely congested, congested, slow moving and uncongested. Four types of accidents are consequently defined based on the occurrence of each level. A hierarchical scheme is designed for identifying the most congested level and sequentially predicting duration of each level. The proposed model is validated using traffic accident data in 2017 from an anonymous source in Beijing, China by embedding three machine learning algorithms, random forest (RF), support vector machine (SVM) and neural network (NN), in the scheme. The results show NN outperforms the other two models when the assessment is conducted in absolute differences. Meanwhile, RF has a slightly better performance than SVM, especially when predicting the short-period congestion of severely congested level at the first time. By continuously updating the traffic condition information, significant improvement in accuracy can be acquired regardless of the exact model used. This study shows that emerging crowdsourcing data can be used in a real-time analysis of traffic accidents and the proposed model is effective to analyze such data.

An investigation of Driver Attitudes Towards Road Safety in Oman

The Summary: Nowadays, road safety and associated behaviors received a lot of attention. Road Traffic Accident (RTA) is a combination of many factors comprising roads designs, vehicle and environmental conditions and human behavior. Human behavior is the most prevalent contributing factor for road traffic accidents. This study aims to identify accident risk factors associated with driving behaviors among the drivers in Oman and its risk perception. The study comprised the use of both qualitative and quantitative methods. The qualitative method included interviews with focus group from the Ministry of Transportation in Oman. The quantitative approach included analysis of road traffic accidents in Oman, followed by a survey questionnaire distributed to a random selection drivers in Oman. Both qualitative and quantitative data were analyzed using constant comparative and statistical techniques. The results showed that gender, age, and experience have significant influence on attitude towards the commission of traffic violations and risk behavior. The young and male drivers were found to be significantly more involved in road accidents, and the 26-33 years age group and those with minimal driving experience of 1-5 years constitute the risk-taking attitudes group. The results of this study will be of interest to the local community and transport planning in the Sultanate of Oman and it will help reduce traffic accidents and improve driver behavior.

A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis

Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers.

Accident Black Spot Detection on Greater Mumbai Region

Traffic accident Black Spot detection on urban settlement using unsupervised learning algorithms and accident analysis using statistical methods on Greater Mumbai Region is carried out in this study. At present the traffic accident analysis in Mumbai is a manual process. Through the clustering analysis on Mumbai accident data from January 2018 to September 2019, we put forward a method to detect accidental blackspots effectively using DBSCAN algorithm.

Risk analysis of traffic accidents’ severities: An application of three data mining models

Traffic accidents are costing the world more than a million lives yearly alongside monetary losses, especially in the Gulf Cooperation Council region. This situation raised the need to examine potential risk factors contributing to traffic accident severities. In this paper, three data mining models were applied to provide a comprehensive analysis of risk factors related to traffic accidents’ severities. One of the used models was a decision tree to examine the correlations between potential risk factors. The other applied models were Bayesian Network and linear Support Vector Machine. The results confirmed that pedestrians were the most vulnerable road users compared to drivers and passengers. Male drivers and front seat-passengers were more exposed to severe or fatal injury. Similarly, elderly drivers had higher odds of having severe or fatal injuries. Road classifications and accident types were also considered significant variables related to traffic accidents’ injuries. Utilizing seat belt could lessen the level of injury. Regarding the performance of the applied models, Bayesian network was more accurate in predicting the variables compared to other models.

Characteristic Analysis and Prediction of Traffic Accidents in the Multiethnic Plateau Mountain Area

AbstractThis paper analyzes the distribution characteristics and influencing factors of traffic accidents in the multiethnic-gathering plateau mountain area. Using data from an investigation of 56,...

Improving the educational process to address issues of digital technologies development in the road industry

The features of the educational process in training personnel for the road industry at Altai State Technical University are considered, taking into account the development of the digital economy. Much attention has been paid to key technologies that provide digitalization for the road industry. The set of disciplines necessary for obtaining knowledge in digital technologies for the further activities of university graduates is analyzed. When studying bachelor’s and master’s programmes, future specialists acquire knowledge of modern methods in computer-aided design of highways, geographic information systems in construction, information modeling and intelligent transportation systems. During the educational process, an understanding of the importance of implementing an automated control of equipment in road construction works using onboard information management system is also formed. The directions of scientific research are described in which students studying Master’s Programme in Highway Design Construction and Management work. These studies make extensive use of digital technology. The analysis of road traffic accidents for identifying hazardous sections is given, as well as the analysis of the current road state with the development of measures for improving the transport and operational road condition. Current research areas using digital technologies are in demand in modern conditions.

Analysis of traffic accidents of young drivers in urban areas and measures to increase safety

Traffic accidents are considered the first cause of death of young people, that is, young drivers. Therefore, this problem needs to be given a lot of attention. Traffic accident which include young drivers must be analyzed as an element of the system, and it is necessary to analyze and investigate potential solutions and improve all elements in order to improve the system itself. In the Republic of Croatia, for the first time in 2004, the term "YOUNG DRIVER" as a driver of a motor vehicle up to the age of 24, who holds a drivers license issued in the Republic of Croatia, was introduced into the Road Traffic Safety Act. The share of young drivers in the population in the Republic of Croatia is 4.3%. Young drivers make up about eight percent of road users, causing a total of 9.6% of traffic accidents, or 12.5% of those with fatalities. This paper will deal with traffic safety in other countries, especially in the surrounding countries. According to statistics related to road accidents, young drivers are over-represented and are at greater risk to themselves, passengers and other participants than other drivers. There is a need to respond in a timely manner and to adopt optimal measures to improve the safety of young drivers in road traffic and thus for the safety of other road users. The aim of this paper is to outline measures that would reduce the number of road accidents involving young drivers, especially in urban areas.

Methodology Designed to Evaluate Accidents at Intersection Crossings with Respect to Forensic Purposes and Transport Sustainability

Currently, there are quite a lot of incorrect procedures and mistakes that occur in the forensic area, which lacks analytical approaches toward solving the causes of accidents using s–t diagrams (distance–time diagrams) combined with the software simulation applications. When analyzing accidents, the correct information is of key importance. The aim of this article is to define a new specific technical and analytical approach toward handling expert’s reports on traffic accidents in road transport at intersections, with respect to the traffic lights. A simulation program application is used as a progressive means of accident evaluation. This procedure must become a standard in the methods of modern traffic accident analysis. The application of this methodology with simulation tools for accident reconstruction enables one to perform a very precise analysis of traffic accidents. Mutual space and time relationships of vehicles’ movements have been evaluated here, depending upon the intersection signal plan. To demonstrate the methodology, a real case is used here, reconstructed by means of the complex analytical simulation software PC-Crash. A procedure processed by these means can be beneficial for forensic traffic accident analysis.

Prediction of Road Traffic Accidents based on Rolling-optimized Grey Markov Mode

In order to solve the problem that the accuracy of the accident prediction is affected by the time-effectiveness of the forecast data, the paper introduces the rolling optimization strategy based on the original data of the road traffic accident in the road section, and further establishes the rolling optimization-grey Markov dynamic prediction model. Using the Markov chain theory to explore the transition law between different states, the development trend of road traffic accidents volume is predicted, and the prediction accuracy of the random time series is further improved. Case analysis proves that the method has better prediction accuracy and practicability over a certain period of time, which can provide reference for road traffic accident prediction analysis and traffic safety early warning

To the question of administrative legal regulation of carsharing and implementation of the federal state monitoring in the area of ensuring road traffic safety to the parties involved

The subject of this research is the normative legal acts that regulating responsibility for failure to comply with statutory requirements on ensuring road traffic safety in the context of short-term car rentals, law enforcement practice, statistical analysis of traffic accidents involving the vehicles provided by carsharing companies, as well as terms for their provision. The object of this research is the social relations emerging in the context of short-term car rentals owned by legal entities and individual entrepreneurs. The goal of this work consist in the attempt to improve the administrative legislation on bringing to responsibility legal entities and individual entrepreneurs who provide short-term rental services to citizens. The scientific novelty lies in legal regulation of the definition of “carsharing” on the level of federal legislation, as well as in amendments to the terms of the contract that would eliminate inconsistency of case law with regards to civil law relations and bringing to administrative responsibility of the persons who fail to comply with statutory requirements on ensuring road traffic safety. The proposed by the author concept of “carsharing” would allow imposition of obligations upon the leaseholders on compliance with road traffic safety requirements placed on the legal entities and individual entrepreneurs while operating a vehicle in accordance with the Federal Law No. 196-FZ of 12.10.1995 "On Road Traffic Safety".

(Side) effects of the rule of the road and neurophysiology on traffic safety: A hypothesis

Nowadays, the roots of left-hand traffic (LHT) and right-hand traffic (RHT) – including fighting on horse-back and whipping horses that are drawing carriages – are at an all-time low; thus, it is time to assess and appreciate the nexus of road-safety practices and human neurophysiology. We hypothesize that safety of LHT and RHT can be associated with neurophysiology. We summarize scattered empirical research into plausible links between neurophysiological aspects such as handedness, eye movement bias, and hemispheric lateralisation and how safe, in theory, LHT vs. RHT may be for whom. The scarcity and limitations of empirical data into road traffic accidents associated with LHT or RHT are surprising. Even though it was claimed that countries with LHT have lower collision rates than countries with RHT some 50 years ago, we lack informative analyses of traffic accidents in countries with either LHT or RHT which consider plausibly associated neurophysiology. Overall, we predict that LHT (with the driver sitting on the right) is safer than RHT. As ‘the rule of the road’ and neurophysiology may have important unrecognized “side” effects, we suggest that (and how) this rationale should be tested.

The improvement of organizational and legal mechanisms of admission of novice drivers to traffic on roads

The research object is social relations connected with the admission of novice drivers to traffic on roads and the issue of driver’s licences. The research subject is the set of normative acts of the Russian Federation regulating the admission of drivers to traffic on roads, and the statistical data of the State Road Safety Unit on road accidents with novice drivers whose driving experience is up to 2 years. The authors analyse road accidents with novice drivers, identify the main reasons of accidents and offer the solutions to these problems. The scientific novelty of the research consists in the fact that the analysis of traffic accidents helped to outline the problems that should be solved within the shortest possible time in order to reduce the number of road accidents caused by novice drivers. In this context, the authors suggest introducing changes into the legislation of the Russian Federation which would allow for the declaration of driver’s licences issued in other states invalid, as well as the exchange of non-Russian driver’s licences for Russian ones only after adequate training and passing the exams. Besides, the authors suggest introducing the possibility for juveniles who passed the exams to drive a vehicle before they arrive the age of 18 with particular restrictions and accompanied by an experienced driver.   

An exploratory analysis of traffic accidents and vehicle ownership decisions using a random parameters logit model with heterogeneity in means

The interaction between traffic accidents and household vehicle ownership decisions has not been thoroughly explored in literature. This interaction is worthy of investigation as the outcome of traffic accidents involving property damage significantly affects the household members’ overall mobility levels and car fleet utilization and renewal process. In this study, we present a methodology that addresses a driver’s decisions pertaining to a damaged car following a traffic accident. The choice set includes three alternatives: to replace the damaged car with a new one, replace the damaged car with a used one, and repair the damaged car. A random parameters (mixed) logit model with heterogeneity in the means is specified and estimated to gain more insight into the driver’s decision-making process following a traffic accident.The estimation results of the random parameters (mixed) logit model with heterogeneity in the means indicate that a wide range of explanatory variables affect the driver’s decision to replace a damaged car with a new or used one. For example, as the repair cost to the car value ratio increases, the likelihood of the driver replacing the damaged car with a new one increases. The marginal effect of not repairing a damaged car is twice that of replacing a damaged car with a new car. Additionally, traffic accidents causing critical injuries to drivers decrease the likelihood of replacing a damaged car with a used car relative to accidents that do not generate critical injuries. Other explanatory variables that are likely to increase the propensity of drivers to replace damaged cars with new ones include the car age, type of insurance policy, total household annual income, driver age, and number of workers at the household. Clearly, a driver’s decision to repair or replace a damaged car should not be viewed as a discrete or isolated decision. The stochastic component of traffic accidents (driver decisions pertaining to damaged cars) should be accounted for when formulating changes in household vehicle ownership levels and utilization decisions.

Traffic Accidents with Autonomous Vehicles: Type of Collisions, Manoeuvres and Errors of Conventional Vehicles’ Drivers

Autonomous vehicles have the potential to dramatically reduce traffic accidents. This assumption is based on the fact that autonomous vehicles eliminate the impact of a human factor on the occurrence of a traffic accident. Autonomous vehicle’ testing in real traffic conditions is carried out worldwide. In this paper, we analyzed traffic accidents with autonomous vehicles that occurred in the US state of California in the period from 2015 to 2017. In order to better recognize the characteristics of traffic accidents with autonomous vehicles, we were carried out a comparative analysis of traffic accidents with only conventional vehicles at locations where occurred traffic accidents with autonomous vehicles. During the analysis of traffic accidents, we have put emphasis on the type of collision, manoeuvres and errors of the drivers of conventional vehicles that led to the traffic accident. Applying statistical analysis, we were found that the type of collision “rear-end” more often in traffic accidents with autonomous vehicles. Types of collisions “pedestrian” and “broadside” were less in traffic accidents with autonomous vehicles. Drivers’ manoeuvres of conventional vehicles do not differ depending on whether an autonomous vehicle is involved in the traffic accident. Drivers’ errors of conventional vehicles that are more often in accidents with autonomous vehicles are “unsafe speed” and “following too closely”. The obtained results were used to propose measures that will improve communication between autonomous vehicles and drivers’ conventional vehicle.

Cause Analysis of Traffic Accidents on Urban Roads Based on an Improved Association Rule Mining Algorithm

The traffic accidents on urban roads are result of joint actions between multiple factors, namely, human, vehicle, road and environment. To identify the key causes to such accidents, it is necessary to mine the association rules between relevant risk factors out of the statistics on these accidents. Considering the multiple layers and dimensions of accident data, this paper improves the Apriori algorithm to mine the association rules between risk factors, and probes deep into the causes of traffic accidents on urban roads. According to the layer and dimension of specific attributes, the parameters like support, confidence and lift were adjusted to find the qualified association rules between risk factors. The results were further screened to obtain a series of meaningful association rules. The research results enable the traffic department to formulate pertinent accident control measures, and promote the traffic safety on urban roads.

Analysis of Fatal Road Traffic Accidents In A Township of South India

Analysis of Fatal Road Traffic Accidents In A Township of South India

The role of driver reliability in road safety

The driver of an automobile is the key part of the “driver–car–road–environment” system, the stable functioning of which determines the efficiency and safety of road traffic. The driver as the operator of the “driver –car–road–environment” system receives most of the information from the road, data from moving and standing objects, road signs, traffic lights, surface conditions and traffic conditions. An analysis of most traffic accidents shows that the weakest part of the “driver–car–road–environment" system, restricting its effectiveness and dependability, is the person. To ensure the necessary dependability and safety, the driver of any vehicle must be careful. This is supported by an appropriate psychophysiological state, which, in turn, depends on many factors. The article presents an analysis of research work taking into account the influence of various factors on the dependability of a vehicle driver. Means and methods of research are described. Recommendations are given on creating a stand for studying the influence of the psychophysiological state of the driver on road safety. Keywords Driver dependability; road traffic; automobile; traffic environment; road accidents; road safety

Epidemiology and cost-analysis of emergency department patients treated following traffic accidents in Iran: A retrospective cross-sectional study

Introduction:Epidemiological analysis of traffic accidents can provide information for future plans to lower the cost and burden of road traffic accidents (RTAs). This study was aimed to determine the epidemiological characteristics of patients with RTAs.Methods:We conducted a retrospective cross-sectional study of RTA patients presenting in 2016 to the Emergency Department at Shahid Bahonar Medical Education Center in Kerman, Iran. A checklist including variables such as age, sex, month, in which the patient referred, final outcome, overall cost and the site of injury used to collect data. The diagnostic criteria were in accordance to ICD 10.Results:Of the total of 3277 patients who were studied, 2713 (82.78%) were men and 564 (16.66%) were women. Most of the accidents occurred at the age group of 16–30 years and the average cost of treatment in the hospital was 2152.45 USD. The most affected area was the lower limb. The majority of accidents occurred in spring and summer. The mortality rate was (2.74%).Discussion:Injuries and deaths due to RTAs are a major public health problem, especially in young age groups. Therefore, more preventive programs targeting young adults should be considered to reduce the burden of RTAs.