Traffic Accident Database Research Articles

Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios

Autonomous vehicles (AVs) must fulfill adequate safety requirements before formal application, and performing an effective functional evaluation to verify vehicle safety requires extensive testing in different scenarios. However, it is crucial to rationalize the application of different scenarios to support different testing needs; thus, one of the current challenges limiting the development of AVs is the critical evaluation of scenarios, i.e., the lack of quantitative criteria for scenario design. This study introduces a method using the Spherical Fuzzy-Analytical Network Process (SF-ANP) to evaluate these scenarios, addressing their inherent risks and complexities. The method involves constructing a five-layer model to decompose scenario elements and using SF-ANP to calculate weights based on element interactions. The study evaluates 700 scenarios from the China In-depth Traffic Safety Study–Traffic Accident (CIMSS-TA) database, incorporating fuzzy factors and element weights. Virtual simulation of vehicles in the scenarios was performed using Baidu Apollo, and the performance of the scenarios was assessed by collecting the vehicle test results. The correlation between the obtained alternative safety indicators and the quantitative values confirms the validity and scientific validity of this approach. This will provide valuable guidance for categorizing audiovisual test scenarios and selecting corresponding scenarios to challenge different levels of vehicle functionality. At the same time, it can be used as a design basis to generate a large number of effective scenarios to accelerate the construction of scenario libraries and promote commercialization of AVs.

High-risk powered two-wheelers scenarios generation for autonomous vehicle testing using WGAN

Objective Autonomous vehicles (AVs) have the potential to revolutionize the future of mobility by significantly improving traffic safety. This study presents a novel method for validating the safety performance of AVs in high-risk scenarios involving powered 2-wheelers (PTWs). By generating high-risk scenarios using in-depth crash data, this study is devoted to addressing the challenge of public road scenarios in testing, which often lack the necessary complexity and risk to effectively evaluate the capabilities of AVs in high-risk situations. Method Our approach employs a Wasserstein generative adversarial network (WGAN) to generate high-risk scenes, particularly focusing on PTW scenarios. By extracting 314 car-to-PTW crashes from the China In-depth Mobility Safety Study–Traffic Accident database, we simulate outcomes using PC-Crash software. The data are divided into scenes at 0.1-s intervals, with WGAN generating numerous high-risk scenes. By using a cumulative distribution function (CDF), we sampled and analyzed the vehicle’s dynamic information to generate complete scenarios applicable to the test. The validation process involves using the SVL Simulator and the Baidu Apollo joint simulation platform to evaluate the AV’s driving behavior and interactions with PTWs. Results This study evaluates model generation results by comparing distributions using Wasserstein distance as an indicator. The generator converges after approximately 200 epochs, with the iterator converging quickly. Subsequently, 10,000 new scenes are then generated. The distribution of several key parameters in the generated scenes can be found to approximate that of the original scenes. After sampling, the usability of generated scenarios is 64.76%. Virtual simulations confirm the effectiveness of the scenario generation method, with a generated scenario crash rate of 16.50% closely reflecting the original rate of 15.0%, showcasing the method’s capacity to produce realistic and hazardous scenarios. Conclusions The experimental results suggest that these scenarios exhibit a level of risk similar to the original crashes and are effective for testing AVs. Consequently, the generated scenarios enhance the diversity of the scenario library and accelerate the overall testing process of AVs.

Magnitude and determinants of road traffic accidents in North Gondar Zone; Amhara Region, Ethiopia

Background Road traffic accidents are a severe health concern in Ethiopia. This study aims to examine the primary contributing variables of road traffic accidents in Ethiopia, North Gondar Zone, under 24 administrative wereda’s. Methods This study utilized secondary data collected from both the werda-level and North Gondar central-level offices within the study areas. The sector police typically submit reports to the zonal-level offices annually. Information regarding crashes, fatalities, and injuries during the study period was extracted from these police reports. The research focused on analyzing the frequency and severity of traffic accidents attributed to various factors of drivers, vehicles, road conditions, pedestrians, and environmental influences. Results In a sample of 603 aggregate road traffic accidents, the data reveals that the majority of damage results are attributed to young drivers, accounting for 58% of the cases, with junior grade level drivers contributing 29.4%. Driving without a license was a significant factor, representing 87%, while accidents involving recruited driver stood at 63.2%. Vehicles with small to articulated trucks contributing to 39%. Mechanical failures were prevalent, with an unknown diagnosis rate of 48.3%. Vehicles in service for 2-5 years accounted for 29.4%. Road surface conditions were 46%, while rural roads saw a higher percentage at 51.2%. Weather conditions played a role also influential, as dry asphalt and gravel roads under main access ownership were linked to of accident, with 91.2% occurring during visible daylight hours between 8:00-12:00 and 15:00-17:00. The victims were predominantly farmers at 49.6%, followed by students at 23%. Conclusions To reduce road traffic accidents in the study area, it is essential to manage causative factors such as driver and road users, vehicles, roads, pedestrians, and environmental behaviors. The study suggests the development of a successful, up-to-date traffic accident database system regionally within respective offices.

Pattern recognition of urban traffic accidents: Application of cluster analysis and support vector machine in accident pattern recognition

Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones.

An investigation of ADAS testing scenarios based on vehicle-to-powered two-wheeler accidents occurring in a county-level district in Hunan province

Abstract To meet the requirements for high-risk testing scenarios in the safety assessment of advanced driver assistance systems (ADAS), this study selected 239 vehicle-to-powered two-wheeler accidents occurring in Xiangtan County from the China In-depth Mobility Safety Study—Traffic Accident (CIMSS-TA) database. Seven scenario elements were selected from the levels of static road elements, dynamic traffic participants and environment elements. The accident characteristics were obtained through statistical analysis. The results showed 75.73% of accidents occurred at T-junctions/intersections, and 34.73% of accidents were caused by the visual obstructions. Six typical vehicle-to-powered two-wheeler crash scenarios were established by cluster analysis, including two straight road scenarios (scenario 3 and 6), two T-junction scenarios (scenario 1 and 5), and two intersection scenarios (scenario 2 and 4). Furthermore, six vehicle-to-powered two-wheeler testing scenarios, which represent the traffic environment characteristics of Chinese county-level districts, were constructed by analyzing the vehicle/PTW speed, the first collision point of vehicle, obstacle type and location, etc. The results showed the vehicle speed in scenario 4 was higher than that in scenario 2, with the average values of 34.32 km/h and 21.99 km/h, respectively. The results of this study provided testing scenarios for the safety assessment of ADAS, and contributed to improving the vehicle active safety technology.

Analysis of bicycle crash characteristics and typical scenarios in France: A descriptive epidemiology and clustering approach

Bicycle crashes can result in severe injuries and fatalities, emphasizing the importance of identifying typical scenarios and characteristics of these crashes to develop effective prevention and mitigation strategies. This study analyzed datasets of bicycle crashes in France from 2005 to 2021 extracted from the French national road traffic accident database, evaluating bicycle accident characteristics through conventional descriptive epidemiology approaches and using the K-medoids clustering method to identify typical scenarios and their associated characteristics. Among 1,121,571 road traffic accidents in France, 80,851 were bicycle crashes, with a decline between 2005 and 2013 but a steady increase since then, peaking in 2020 at approximately 1.74 times that of 2005. Victims of bicycle crashes were primarily between 11 and 62 years old, with the largest population among 14–33 years old. Male victims were more involved in bicycle crashes than female victims, with the majority of crashes involving a moving vehicle. The majority of bicyclists involved in accidents were found not wearing helmets, and even bicyclists wearing helmets still sustained injuries. Typical bicycle crash scenarios included four bicycle-vehicle head-on-side collisions, one bicycle-obstacle collision, and two bicycle-pedestrian collisions, with bicycle-obstacle collisions often resulting in serious injuries similar to bicycle-vehicle collisions. Pedestrians were more likely to sustain injuries than bicyclists during bicycle-pedestrian collisions, and their injuries were often more severe than those of bicyclists. The study provides insights into the characteristics and typical scenarios of bicycle crashes in France and can be used to develop targeted interventions to improve road safety for cyclists.

Car accidents in drivers with Parkinson's disease or multiple sclerosis: A Swedish nationwide study.

Parkinson's disease (PD) and multiple sclerosis (MS) can impair driving. However, we lack evidence on car accidents associated with these diseases. The aims of this study were to examine what types of car accident were associated with drivers with PD and MS, compared to individuals with ulcerative colitis (UC; the comparison group), and to evaluate the occurrence of car accidents in relation to years since diagnosis. This retrospective nationwide, registry-based study included drivers involved in car accidents between 2010 and 2019, based on the Swedish Traffic Accident Data Acquisition database. Data on pre-existing diagnoses were retrieved retrospectively from the National Patient Registry. Data analyses included group comparisons, time-to-event analysis, and binary logistic regression. In total, 1491 drivers, including 199 with PD, 385 with MS, and 907 with UC, were registered to have been involved in a car accident. The mean time from diagnosis to the car accident was 5.6 years for PD, 8.0 years for MS, and 9.4 years for UC. Time to car accident since diagnosis differed significantly (p < 0.001) among groups (adjusted for age). Drivers with PD had more than twice the odds of a single-car accident than drivers with MS or UC, but no differences were observed between MS and UC. Drivers with PD were older and experienced the car accident within a shorter timeframe after disease diagnosis. Although several factors may cause a car accident, fitness to drive could be more thoroughly evaluated for patients with PD by physicians, even early after the diagnosis.

Computerization of FRSC (Federal Road Safety Corps) Database for Road Traffic Accidents and Infractions

This project aimed at designing a computerized web-based road traffic accident and infraction database system for the Federal Road Safety Corps (FRSC) which will enable FRSC to get summarized traffic accident and infraction information at various level easily and quickly. The collection, management and dissemination of road traffic accident and infraction related events have represented a serious issue to road safety officers and all stakeholders alike due to the absence of a central repository from which all accident and infraction data can be stored and managed. Where accessible, these data available in the paper-based form and this represents a bottleneck in updating the available data. The purpose of the system is to design a computerized system that will collect, record, disseminate and manage road traffic accident and infraction information for the FRSC. The system is achieved using structured system analysis and design methodology. The software that is used to design the system is Adobe Dreamweaver and XAMPP local server, the programming language that was used comprise of Hyper Text Mark-up Language (HTML), JavaScript, Cascading Style Sheet (CSS) and Hypertext Pre-Processor (PHP). The website was tested using on a local web server “XAMPP” and Structured Query Language is used for the Database design. The system has different subsystem to meet its core functionalities. An administration subsystem authenticates administrators and road safety officers to login to the system and executes their defined task. The accident and infraction report subsystem allow the officer to enter and submit road traffic accident and infraction details using a simple to use web-based interface.

Accident Prone Area Database Analysis in Yogyakarta City

The need for transportation in Indonesia is increasing day by day. This is due to the high population growth, the rapid number of motorized vehicle ownership, as well as the construction of new settlements, which resulted in the need for transportation facilities always increasing. Transportation problems occur as traffic density increases, one of which is traffic accidents. As a first step in reducing the number of accidents, it is necessary to identify accident-prone areas. It is needed to compile a GIS-based traffic accident database that is informative. In this research, two methods of analysis are generally carried out, including analysis of traffic accident-prone areas and analysis in compiling a database based on geographic information systems. In this study, results were obtained regarding the characteristics of traffic accidents in the city of Yogyakarta which included the number of victims of traffic accidents during the period 2016 – 2020, as many as 3,530 people, with the type of vehicle involved in the most accidents being motorcycles, namely 3,671 vehicles. The type of front-rear collision is the most common type of accident and the age most often involved in accidents is in the age range of 20-29 years. Then based on the results of the analysis using three methods of determining DRK, including the equivalent accident number (EAN) method, the frequency method, and the z-score method, obtained five roads that are categorized as accident-prone areas, namely Jenderal Sudirman, Brigjend Katamso, HOS Cokroaminoto, Kusumanegara, and Magelang.

Autonomous driving testing scenario generation based on in-depth vehicle-to-powered two-wheeler crash data in China

A reliable critical-scenario-based safety assessment of autonomous vehicles in China requires a thorough understanding of complex crash scenarios in Chinese background traffic. Based on actual crashes between a vehicle and a powered two-wheeler (PTW) in China, this study generated the autonomous driving testing scenarios from functional, logical and concrete levels. First, 239 video-recorded crash cases were selected from the China In-depth mobility Safety Study - Traffic Accident (CIMSS-TA) database. Using the k-medoids clustering method, six functional scenarios were generalized according to seven crash characteristics (time of day, road type, road surface, obstruction, motion of vehicle, motion of PTW, relative moving direction and position of PTW with respect to vehicle), which contained two straight road scenarios, two T-junction scenarios and two intersection scenarios. Then, using a trajectory analysis program written by Python, the dangerous time instant of each crash was extracted based on the relative trajectory. According to five dynamic parameters of dangerous time instant, namely vehicle velocity (Vehicle_V), PTW X'-coordinate velocity (PTW_VX'), PTW Y'-coordinate velocity (PTW_VY'), PTW X'-coordinate relative position (PTW_LocX') and PTW Y'-coordinate relative position (PTW_LocY'), a crash trigger scheme was built to remain a case challenging when the involved vehicle is replaced by an autonomous vehicle with completely different maneuvers. Using the kernel density estimation (KDE), the logical scenarios were evolved by calculating the distribution of these dynamic parameters in each cluster. The results showed that there were differences in the distribution of dynamic parameters between six functional scenarios. For instance, the Vehicle_V in the scenario where a vehicle turning right impacts with a right/right rear PTW traveling straight ahead was higher than that in the scenario where a vehicle changing to the left lane impacts with a left/left rear PTW traveling straight ahead, with ranges of (10 km/h, 30 km/h) and (5 km/h, 15 km/h), respectively. Finally, considering the correlation of dynamic parameters, a virtual crash generation approach based on the independent component analysis (ICA) representing the original crashes with independent parameters was proposed to obtain sufficient concrete testing scenarios. The results showed that the statistical characteristics of virtual crashes were consistent with those of original crashes. Therefore, the virtual crash generation approach was effective. And a concrete crossing testing scenario with the crash trigger conditions of Vehicle_V = 26.272 km/h, PTW_VX' = 15.567 km/h, PTW_VY' = −1.670 km/h, PTW_LocX' = −27.265 m and PTW_LocY' = 52. 149 m was especially demonstrated. This study provides a theoretical basis for generating autonomous driving testing scenarios and data support for establishing relevant testing schemes tailored to the traffic environment in China.

Risky behavior analysis for cross-border drivers: A logit model and qualitative comparative analysis of odds of fault and injury vulnerability in Guangdong, Hong Kong and Macau

Introduction: Due to globalization and the acceleration of cross-border exchanges, cross-border risk behaviors have received widespread attention. Previous research has concluded that foreign cross-border drivers engage in relatively more risk-taking behavior patterns and are likely to experience a higher crash rate or be more inclined to cause severe crashes. However, there is little evidence on the comparison of drivers who belong to the same ethnic group driving across within-country borders. Method: Based on the cross-border motor-vehicle crash reports in 2006–2010 from the Road Traffic Accident Database of the China Ministry of Public Security, this paper examines the risk factors of being at fault and getting killed or seriously injured in cross-border traffic crashes and casual paths toward crash liability and injury severity for Hong Kong and Macao drivers driving in the Chinese mainland. Results: There are extremely complex factors behind drivers from Hong Kong and Macao causing at-fault crashes or sustaining fatal and serious injuries in the Chinese mainland. Factors such as gender, age, illumination, and weather conditions do not individually affect the risk of driver at-fault crashes or severe casualties in the crashes among Hong Kong and Macao drivers driving in the Chinese mainland. Nonetheless, collectively, these factors influence them along with different vehicle types, roads, and environmental factors. Conclusions: This paper provides more theoretical findings for understanding the compound effect of multiple risk factors involving cross-border at-fault crashes or serious casualties. The conclusions of this research are valuable as representative references for cross-border risk management policies. Practical applications: To reduce the effects of different factors on cross-border risky driving behaviors and/or injurious crashes, various measures should be focused on, including specialized driver training, enhancement of the roads/environment, development of effective road safety campaigns, and directives facilitating cross-border cooperation in the field of road safety.

Investigating injury severity of pedestrian–vehicle crashes by integrating latent class cluster analysis and unbalanced panel mixed ordered probit model

In recent years the pedestrian deaths have been declining, but the pedestrian–vehicle death rate in Croatia is still pretty high. This study intended to investigate the injury severity of pedestrian–vehicle crashes and identify the influencing factors. To achieve this goal, the dataset was firstly collected from Traffic Accident Database System maintained by the Ministry of the Interior, Republic of Croatia from 2015 to 2019, and then latent cluster analysis was employed to identify homogenous clusters from heterogeneous dataset. Based on the classified dataset, unbalanced panel mixed ordered probit model was proposed. By analyzing the classes with different vehicles, the proposed model revealed a more complete understanding of significant variables and showed beneficial performance from the goodness-of-fit, while capturing the impact of exogenous variables to vary among different places, as well as accommodating the heterogeneity issue due to unobserved effects. Findings revealed that the proposed model can be considered as an alternative to determine the factors of injury severity and to deal with the heterogeneity issue. The results may provide potential insights for reducing the injury severity of pedestrian-vehicle crashes.

Electric scooters accidents: Analyses of two Swedish accident data sets

Since august 2018 electric scooters (e-scooters) are available in selected cities in Sweden, operated by several different operators. There is a growing concern regarding their safety as they grow in popularity. The aim with this study was to investigate injuries associated with e-scooters in Sweden and to identify accident characteristics. In addition, the aim was to observe how different data collection procedures and samples may influence the results. Two complementary data sets were used; insurance data including all reported injuries to Folksam Insurance Group during the period January 2019 to May 2020 and the Swedish Traffic Accident Data Acquisition database (STRADA), the national system for road traffic injury data collection, was used to study accident related to e-scooter use in the Stockholm city area between May and the end of August 2019. Most of the injuries associated with e-scooters occurred in single crashes, but in 13% of the accidents another road user was injured, either due to interactions with e-scooters or due to a parked e-scooter being a hazard. In both data sets more than half of the accidents occurred during weekends. In total 46% of all who had visited an emergency department the accident occurred during night-time (10 pm to 6 am). The overall large proportion of injuries to the head and face indicates the need for actions aimed to increase helmet use among e-scooter riders. Local authorities should take a wider responsibility since one third of all accidents primarily occurred due to lack of maintenance or that the rider hit a curb stone. In comparison to hospital data, insurance claims include riders with all types of injuries irrespectively what type of healthcare the rider was seeking. Hence, to better understand the consequences and to make the right decisions regarding countermeasures aimed to improve the safety of e-scooter riding, data from different data source are needed.

Study of typical electric two‐wheelers pre-crash scenarios using K-medoids clustering methodology based on video recordings in China

Crash safety of electric two-wheelers (ETWs) has been one of the most important safety issues in China due to their high proportion of involvement in traffic accidents. Automated Emergency Braking (AEB) systems have proven to be effective in reducing the number of fatalities and injuries in traffic accidents. Providing test scenarios is one of the fundamental tasks required for establishing a set of AEB test programs for ETWs. Compared to traditional in-depth accident data, accident data accompanied with video recordings provide more accurate accident information prior to a crash as both the traffic environment and the crash process can be observed from the video. In this study, a set of typical AEB test scenarios for ETWs was developed using accident data with video information. Video recordings of 630 car-to-ETW crashes in China from 2010 to 2021 were selected from the VRU Traffic Accident database with Video (VRU-TRAVi). A K-medoids11See Section 2.3 Cluster Analysis. cluster analysis was carried out based on variables including the collision time, visual obstruction, motion of the car and ETW before the collision, relative motion direction between the car and ETW, and the ETW type. The velocity information of cars and ETWs was also accounted for in each clustering scenario. Seven typical pre-crash scenarios were obtained, including five electric-scooter (E-scooter) scenarios (representing two scenarios where the ETWs are approaching the car from the left side, two scenarios where the ETWs are approaching the car in the same direction and another scenario where the ETWs are approaching the car in the opposite direction) and two electric-bike (E-bike) scenarios where the E-bikes are approaching the car in the perpendicular direction. Both E-bike scenarios are consistent with the E-scooter scenario except for the ETW type and velocity range; therefore, by combining the E-bike and E-scooter scenarios, five ETW scenarios were finally recommended as AEB test scenarios. By comparing with typical scenarios extracted based on the China In-Depth Accident Study (CIDAS) data and the China New Car Assessment Program (C-NCAP) test scenarios, the results show that future AEB test scenarios for ETWs should focus on scenarios with visual obstructions and scenarios where either the car or the ETW is turning, with a velocity range of 15–30 km/h for ETWs.

Enhancing trauma registries by integrating traffic records and geospatial analysis to improve bicyclist safety.

Trauma registries are used to identify modifiable injury risk factors for trauma prevention efforts. However, these may miss factors useful for prevention of bicycle-automobile collisions, such as vehicle speeds, driver intoxication, street conditions, and neighborhood characteristics. We hypothesize that (GIS) analysis of trauma registry data matched with a traffic accident database could identify risk factors for bicycle-automobile injuries and better inform injury prevention efforts. The trauma registry of a US Level I trauma center was used retrospectively to identify bicycle-motor vehicle collision admissions from January 1, 2010, to December 31, 2018. Data collected included demographics, vitals, injury severity scores, toxicology, helmet use, and mortality.Matching with the Statewide Integrated Traffic Records System was done to provide collision, victim and GIS information. The GIS mapping of collisions was done with census tract data including poverty level scoring. Incident hot spot analysis to identify statistically significant incident clusters was done using the Getis Ord Gi* statistic. Of 25,535 registry admissions, 531 (2.1%) were bicyclists struck by automobiles, 425 (80.0%) were matched to Statewide Integrated Traffic Records System. Younger age (odds ratio [OR], 1.026; 95% confidence interval [CI], 1.013-1.040, p < 0.001), higher census tract poverty level percentage (OR, 0.976; 95% CI, 0.959-0.993, p = 0.007), and high school or less education (OR, 0.60; 95 CI, 0.381-0.968; p = 0.036) were predictive of not wearing a helmet. Higher census tract poverty level percentage (OR, 1.019; 95% CI, 1.004-1.034; p = 0.012) but not educational level was predictive of toxicology positive-bicyclists in automobile collisions. Geographic information systems analysis identified hot spots in the catchment area for toxicology-positive bicyclists and lack of helmet use. Combining trauma registry data and matched traffic accident records data with GIS analysis identifies additional risk factors for bicyclist injury. Trauma centers should champion efforts to prospectively link public traffic accident data to their trauma registries. Prognostic and Epidemiological, level III.

Design for AEBS Test Scenario Applying Domestic Traffic Accidents

This study is a study on the development of AEBS test scenarios for traffic accidents in Korea, and was compared and analyzed using the Traffic Accident Analysis Program. To ensure the safety of passengers and pedestrians in traffic accidents, the number of cars equipped with ADAS is increasing rapidly at all car manufacturers in each country. For traffic accidents used in this study, the domestic traffic accident database (ACCC) produced by SAMSONG was used. Domestic traffic accidents differ from overseas traffic accidents in terms of road type, signal system, driver's seat location and number of vehicles. ACCC databases, which supplemented and reinforced these differences, built a database based on the PC-CRASH program. In the study, we analyze the types of accidents to develop comparative scenarios for each type of road and collision type of traffic accidents. When the road types of traffic accidents in Korea were divided into five types and the collision types were divided into six, it was confirmed that the most types of FRONT-SIDE crashes appeared at the intersection. It is expected that the frequency of possible traffic accidents and collision types can be predicted according to the road type in the accident database, we that it can be used as an AEBS test scenario development suitable for the domestic road environment.

Hit-and-runs more common with pedestrians lying on the road: Analysis of a nationwide database in Japan

PurposeTo determine the trends with fatally or otherwise injured pedestrians lying on the road and the relationship to hit-and-run incidents in Japan. MethodsWe extracted data for 2012–2016 from the records of the Institute for Traffic Accident Research and Data Analysis, Japan, a nationwide traffic accident database. All the injured and fatally injured pedestrians were selected. We examined the levels of pedestrian injury, vehicle speed immediately before the collision, whether or not the pedestrian was lying on the road, and hit-and-run incidents. Chi-square test was employed to make a statistical comparison between the two groups. ResultsThe database contained data on 286,383 pedestrian casualties and 7256 fatalities; 8.3% of fatalities (602 persons) and 0.6% of casualties (1827 persons) involved pedestrians lying on the road. The rates of fatalities and severe injuries were significantly higher for pedestrians who were lying on the road than for those who were not. Hit-and-run incidents were evident in 4.0% of casualties and 7.3% of fatalities. The rate of hit-and-run cases was also significantly higher among pedestrians who were lying on the road. Among fatally injured pedestrians not lying on the road, the rates with speeds of ≥30 km/h did not differ significantly between hit-and-run and other cases. However, when the pedestrians were lying on the road, the rate was significantly increased in hit-and-run cases. ConclusionThis is the first report to focus on pedestrians lying on the road and being involved in hit-and-run incidents. In addition to preventing hit-and-run incidents, prevention of pedestrians lying on the road could also decrease fatalities.

Analysis of accident duration on urban arterials roads

Acidentes de trânsito são o principal fator para a formação de congestionamentos não recorrentes em vias urbanas e responsáveis por gerar atrasos inesperados aos usuários do sistema. Entender o comportamento da duração desses acidentes e identificar os seus fatores explicativos pode conduzir para um gerenciamento mais eficiente do tráfego urbano. Buscando contribuir com essa problemática, este trabalho teve como objetivo analisar a duração de acidentes de trânsito ocorridos em vias urbanas da cidade de Fortaleza-CE. Inicialmente, um método para detectar a duração dos acidentes a partir de dados de tráfego de equipamentos de fiscalização eletrônica foi proposto, dada a indisponibilidade dessa informação no banco de dados de acidentes de trânsito. Como resultados, a duração dos 324 acidentes detectados teve uma média de 72 minutos e uma dispersão de 48 minutos. A função densidade de probabilidade Gamma apresentou o melhor ajuste à duração dos acidentes, indicando que essa variável é influenciada por eventos que ocorrem ao longo do tempo, aumentando a probabilidade de o acidente ser resolvido. A análise mostrou que fatores relacionados ao atendimento dos acidentes, ao comportamento dos indivíduos, ao número de veículos envolvidos e ao local de ocorrência (meio de quadra ou interseção) podem estar influenciando a duração dos acidentes. Entretanto, não foram encontrados indícios de que a duração dos acidentes é influenciada pela sua gravidade (acidentes com ou sem vítimas) ou pela sua natureza.

A comparative analysis of three typical crash tests results based on small overlap frontal collision accidents

With the promotion of collision safety technical regulations, the vehicle safety technology has achieved a leap-forward development, and the occupants have been well protected in various collision conditions. However, the statistical data indicate that serious accidental casualties are still present in frontal collision traffic accidents with small overlap ratios. One of the main reasons is that vehicles in the small overlap collisions did not have the energy absorption effect of the longitudinal beams and body structure, causing serious deformation of the passenger compartment and injuries to the occupants. In this paper, the characteristics of small overlap accidents were analysed based on the China traffic accident database. Then, actual vehicle crash tests were carried out by adopting three typical small overlap crash test methods (vehicle-to-vehicle test method, IIHS test method and NHTSA test method), and in-depth analysis was conducted on the vehicle kinematics, collision strength (vehicle body acceleration and vehicle pulse index), occupant injuries, and vehicle body deformation in the crash tests. The results show that although the incidence of small overlap crash is low, the ratio of AIS3+ injury is very high, and the NTHSA test method is more similar to the vehicle-to-vehicle test than the IIHS test method.

Modelling the risk factors for injury severity in motorcycle users in Ghana

This study aims to determine risk factors associated with the injury severity of motorcycle users in Ghana. Data on all reported crashes involving motorcycle users in Ghana were analyzed. The data were extracted from the National Road Traffic Accident Database at the Building and Road Research Institute (BRRI) of the Council for Scientific and Industrial Research (CSIR). Generalized ordered logit models were specified separately for riders and pillion passengers to determine the relationship between injury severity, as an ordered categorical outcome, and a set of possible explanatory variables. The results from the model showed that the injury severity of both riders and pillion passengers was significantly influenced by the day of the week when the crash occurred, weather conditions, road geometry, location type, and traffic control. In addition, the injury severity of riders was also influenced by their age, presence of passenger, and light conditions, whilst the injury severity of pillion passengers was influenced by the time of the crash. The findings from this study provide useful information to improve the understanding of risk factors associated with motorcycle user injury severity. Such data are also important to support the development of appropriate countermeasures to help prevent motorcycle crashes.