Single-vehicle Collisions Research Articles

Road vehicle collision suicide in Australia: Trends, collision types, and individual characteristics.

Suicide by road vehicle collision in Australia is under-explored with mixed findings. We aimed to address this research gap by examining time trends, different types of vehicle collision, and individual characteristics related to vehicle-collision suicide. We retrospectively analyzed deaths by suicide between 1st January 2001 and 31st December 2017 in Australia, using coronial records from the National Coronial Information System. The travel mode used and collision counterpart were retrieved from records of death by vehicle-collision suicide using all available information. We conducted negative binomial regression analysis to examine annual changes in suicide rate by vehicle collision on a public road (N = 640) and other methods of suicide (N = 41,890), and logistic regression analysis to examine individual characteristics associated with the likelihood of dying by suicide via road vehicle collision. Overall, the national suicide rate involving road vehicle collision significantly increased, while the rate by other methods significantly decreased. Drivers accounted for 61% of suicide events by vehicle collision, of which 72% were single-vehicle collisions (commonly involving a tree). For multiple-vehicle collision suicide events, 82% involved collision with a truck. Pedestrians accounted for more than one-third of suicide events, of which 58% involved collision with a truck and 23% involved collision with a car/van. Individuals who were male (odds ratio 1.15; 95% CI 0.88-1.50), aged <25 years old (odds ratio 5.27; 95% CI 3.05-9.10), non-Indigenous (odds ratio 3.36; 95% CI 1.71-6.62), and born overseas (odds ratio 1.40; 95% CI 1.10-1.79) were more likely to die by vehicle-collision suicide than by other methods of suicide. This study provides a better understanding of road vehicle collision suicide in Australia and informs future research directions on topic. Our findings can be used to inform suicide prevention initiatives to reduce vehicle-collision suicide deaths.

Analysis of crash severity in rear-end and angle collisions on an urban roundabout in heterogeneous non-lane-based traffic conditions

ABSTRACT This study examines crash risk factors and develops a severity model for rear-end and angle collisions in roundabouts and its vicinity in non-lane-based heterogeneous traffic. The rear-end severity is primarily influenced by posted speed limits, violation type, collision partner, single-vehicle collision, and vehicle age. Whereas type of violation, collision partner, and presence of road surface condition are the critical influencing factors for determining the severity outcomes of angle collision. The marginal effects of angle collision results indicated that the probability of fatal injury increased by 28% due to heavy-vehicle-vulnerable road user collisions. In the rear-end case, risk factors such as collision partners (heavy vehicles-vulnerable road users) increased by 18% and single-vehicle collisions increased by 25%, highly influencing the severity of fatal injury at roundabouts. Notably, rear-end collisions exhibit higher severity compared to angle collisions. This study provides valuable insights for developing strategies to mitigate roundabout collision severity.

Severity Analysis of Secondary Crashes on High-Speed Roadways: Pattern Recognition Using Association Rule Mining

Secondary crashes (SCs) are a major concern, posing additional safety threats to both non-involved vehicles and incident responders. The objective of this study was to identify the affiliated factors contributing to SCs on roadways with a speed limit of 55 mph or above. Traditional police-investigated crash dataset was analyzed, spanning more than four years (January 2016–February 2020) for the entire state of Alabama. As the crash database did not directly include information on SCs and did not allow for linking a primary crash with a subsequent SC, a data extraction process was developed to identify SCs and understand their characteristics. Association rule mining (ARM) was applied to identify crash patterns based on maximum injury severity levels. The generated rules were filtered based on support, confidence, and lift, and then validated by the lift increase criterion. The results revealed complex relationships between risk factors and severity of SCs. In relation to SCs with injuries, single-vehicle crashes were frequently observed during peak hours and when drivers swerved to avoid objects/persons/vehicles. In contrast, concerning SCs with possible/no injuries, single-vehicle collisions were more likely to occur when drivers failed to notice objects/persons/vehicles and were involved in speeding. On urban interstates, single-vehicle SCs were frequently associated with injuries, while rear-end SCs were often linked to possible/no injuries. The findings of this study can be helpful in enhancing existing traffic incident management programs to mitigate the occurrence of SCs.

Analizing Traffic Accidents on Pekanbaru-Dumai Toll Road from Road infrastructure Perspective

The Pekanbaru-Dumai (PERMAI) toll road serves a crucial role as the busiest access route in Riau Province. Due to the high volume of traffic on this toll road, it is suspected that various types of traffic accidents occur. This study analyzed the frequency of traffic accidents, accident rate metrics for black-spot and black-site areas, and the condition of road infrastructure on the Pekanbaru-Dumai toll road. This study employed a qualitative descriptive method and collected data from primary sources through field observations and secondary sources, such as accident reports from the Riau Regional Police Traffic Directorate (Indonesian: Direktorat Lalu Lintas POLDA Riau) covering the period from 2020 to 2022 and average daily traffic volume data from the Toll Road Regulatory Agency (Indonesian: Badan Pengatur Jalan Tol [BPJT]). The accident rate formula for black spots and black sites was applied to identify accident-prone and high-traffic areas on the Pekanbaru-Dumai toll road. The analysis results revealed a total of 30 incidents over the three years. The highest accident rate for black-spot areas was 0.10 at KM 34 and KM 35, while the highest for black-site areas was 0.10 at KM 35. Most incidents occurred during the night until early morning, specifically from 18:01 to 02:00. Passenger cars were the most frequently involved vehicles, with 16 single-vehicle collisions. These accidents resulted in 28 victims, including 7 fatalities, 9 serious injuries, and 12 minor injuries. In terms of travel direction, Line A (Pekanbaru to Dumai) experienced 11 incidents, while Line B (Dumai to Pekanbaru) had 19 incidents. The primary factor contributing to these accidents was human error, typically due to negligence or carelessness. Based on observations of road equipment and traffic management on the Pekanbaru-Dumai toll road, the study suggests improving safety by adding noise barriers following Indonesia’s Minister of Transportation Regulation No. 82/2018, Article 33. Additionally, enhancing overall street lighting is recommended, in alignment with Indonesia’s Minister of Public Works Regulation No. 16/PRT/M/2014 concerning Minimum Service Standards.

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

Vehicle running attitude prediction model based on Artificial Neural Network-Parallel Connected (ANN-PL) in the single-vehicle collision

Artificial neural networks have drawn growing attention for their outstanding predictive capability combined with traditional research methods. This paper aims to propose a vehicle running attitude prediction model based on Artificial Neural Network-Parallel Connected (ANN-PL), predicting the longitudinal displacement (Svx) and vertical displacement (Svz) of the vehicle body, the vehicle head-up angle (α), and the overriding risk (Cd). The 3D multibody dynamics model (MBD) of the single-vehicle impact on the rigid wall, namely 3D-MBD-SV, was established and validated by the experimental full-scale vehicle collision test. Based on the reliable 3D-MBD-SV, the design of experiment (DOE) approach was carried out to obtain the datasets for training the ANN-PL. The ANN-PL exhibited excellent computational efficiency and satisfactory prediction accuracy compared to the multibody dynamics and finite element simulation calculation methods. However, the different network hyperparameters of the ANN-PL network are essential to prediction accuracy, considering the number of hidden layers and neurons in this paper. In terms of the variables factor analysis, the change of Mean Square Error (MSE) method (COM) in the ANN-PL was used to explore the relationship between the eleven essential input variables and vehicle running attitude. It was found that the maximum relative contribution in ANN-PL (Svx, Svz, α, Cd) is vehicle body mass (Mc) at 70.65%, impact velocity (Vx) at 43.39%, vertical offset of the vehicle body center mass (CMz) at 30.14%, and primary suspension axle box spring vertical travel (Dpz) at 13.63%, respectively. The outcome of this study is expected to provide a research method to solve the complicated engineering issue by building a new artificial neural network algorithmic framework combined with the multibody dynamics and finite element simulation calculation methods.

Descriptive analysis of reports on autonomous vehicle collisions in California: January 2021–June 2022

The characteristics of autonomous vehicles’ collisions from 2021 and the first half of 2022 in California confirm trends reported in previous years. Driving in autonomous mode was associated with fewer instances in which the AV was deemed to be at fault in a collision. Most collisions in autonomous mode were rear-end collisions at intersections. Single vehicle collisions occurred mostly in manual mode. Collisions with vulnerable road users occurred mostly while the autonomous vehicle was in manual mode, often right after disengagement from autonomous mode. In collisions with other vehicles that occurred after disengagement, the other vehicles were frequently deemed to be at fault. Compared to 2021, the collision reports from the first half of 2022 indicate higher shares of collisions in autonomous mode, rear-end collisions, and collisions with vulnerable road users.

Driver, Collision and Meteorological Characteristics of Motor Vehicle Collisions among Road Trauma Survivors.

Road trauma remains a significant public health problem. We aimed to identify sub-groups of motor vehicle collisions in Victoria, Australia, and the association between collision characteristics and outcomes up to 24 months post-injury. Data were extracted from the Victorian State Trauma Registry for injured drivers aged ≥16 years, from 2010 to 2016, with a compensation claim who survived ≥12 months post-injury. People with intentional or severe head injury were excluded, resulting in 2735 cases. Latent class analysis was used to identify collision classes for driver fault and blood alcohol concentration (BAC), day and time of collision, weather conditions, single vs. multi-vehicle and regional vs. metropolitan injury location. Five classes were identified: (1) daytime multi-vehicle collisions, no other at fault; (2) daytime single-vehicle predominantly weekday collisions; (3) evening single-vehicle collisions, no other at fault, 36% with BAC ≥ 0.05; (4) sunrise or sunset weekday collisions; and (5) dusk and evening multi-vehicle in metropolitan areas with BAC < 0.05. Mixed linear and logistic regression analyses examined associations between collision class and return to work, health (EQ-5D-3L summary score) and independent function Glasgow Outcome Scale - Extended at 6, 12 and 24 months. After adjusting for demographic, health and injury characteristics, collision class was not associated with outcomes. Rather, risk of poor outcomes was associated with age, sex and socioeconomic disadvantage, education, pre-injury health and injury severity. People at risk of poor recovery may be identified from factors available during the hospital admission and may benefit from clinical assessment and targeted referrals and treatments.

Investigation of Injury patterns in Heavy-duty Single Vehicle crashes based on real-world accident data in Tamilnadu, India

According to the reports of NITI (National Institute of Transforming India) Aayog Freight 2018, Road freight is the prime mode (59%) of transport in India with the highest per ton-mile cost than rail or water freight (NITI Aayog, 2018). This road freight usually uses heavy-duty vehicles to transmit voluminous goods and services to the destination in time. Due to this, the heavy-duty vehicle population increased on the Indian roads. Heavy-duty vehicle crashes cause a substantial economic burden to the nation and result in more severity to the involved because of differences in weight, speed, and size. Among heavy-duty vehicle crashes, a significant proportion of crashes are heavy-duty single-vehicle crashes. Single-vehicle crashes are those crashes where the vehicle drivers either involve in self-skidding or hit a stationary object (like a tree). The purpose of this study is to investigate the injury pattern in heavy-duty single vehicle crashes. For this study, the data is extracted from the RADMS (Road Accident Database Management System) database and linked with hospital data. This data includes demographic information, road, environmental and injury characteristics. Later, descriptive statistics performed on the dataset to analyse all heavy-duty single-vehicle crashes between January 2013 and December 2018. Overall, 4704 single heavy-duty vehicle crashes occurred during this period, among which 1244 were fatal crashes. Results show that male drivers aged 26 to 64 years old suffered more fatalities (88%), followed by the 18-25 age group (8%). Examination of injury information found that heavy-duty vehicle drivers mostly sustained multiple injuries (9.05%), head injuries (5.05%), followed by leg injuries (4.29%). The results showed that specific road and environmental factors increase the chance of fatal crashes among heavy-duty vehicle drivers. Furthermore, the proposed study gives insight into the injury characteristics and key contributing factors causing heavy-duty single-vehicle crashes. Finally, this study provides appropriate countermeasures and techniques that can mitigate heavy-duty single-vehicle collisions.

Investigation of factors contributing to injury severity in single vehicle motorcycle crashes in India

Single-vehicle crashes are of major concern in both developed as well as in Low Middle Income Countries due to the severity of injuries, particularly fatal accidents. In India, a significant proportion of crashes are single-vehicle crashes. The vehicles which are involved in accidents due to causes such as self skidding, hitting stationary objects, trees that are simply contributed by the drivers themselves are referred to as out-of-control single-vehicle crashes. The main objective of this study is to evaluate the risk factors associated with single-vehicle motorcycle collisions in Tamilnadu, India and identifies the unique characteristics and injury outcomes associated with these collisions. Crash dataset for the present study was prepared from the police-reported crashes for the past nine years that occurred within the state of Tamilnadu between 2009 and 2017. The identified contributory factors which influence injury severity include driver characteristics, crash-related factors, traffic-related factors, vehicle and environment-related factors. In this study, injury severity is classified into three categories, i.e. fatal, serious, and minor injuries. Since the outcome of the injury severity could be measured on an ordinal scale, a discrete ordered outcome model, an ordered logit model is applied. To summarise the results, thirteen of the studied factors are found to have a significant influence on the injury severity of drivers. Results show that the likelihood of fatal injuries increases in crashes where motorcyclists hit stationary fixed objects, hit trees, ran-off road, inclement weather conditions, urban areas. It is also found that winter season, north districts of Tamilnadu, single and two-lane roads, highways, village roads and, other district roads, daylight conditions, drivers who are younger and working-age group, overtaking from left, taking u-turn are associated with less likelihood of fatal crashes. To increase the overall safety of the roads, targeted countermeasures may be designed in light of injury severity of the drivers with respect to single-vehicle crashes also. This study provides useful insights for reducing injury severity in single-vehicle motorcycle crashes.

Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan

Motorcycle is a popular mode of transportation in many developing countries, including Pakistan. Since the last decade, the registered number of motorcycles in Pakistan has increased by six times, constituting 74% of the total registered vehicles. However, limited research efforts have been made to investigate motorcycle-related safety issues in Pakistan. Thus, the relationship between potential risk factors and injury outcomes of motorcycle crashes is still unclear in the country. This study, therefore, established a random parameter logit model to examine the factors associated with the motorcycle injury severity. The analysis is based on two years (2014–2015) of data collected through the road traffic injuries surveillance system from Karachi city, Pakistan. The results indicate that the summer season, weekends, nighttime, elderly riders, heavy vehicle, and single-vehicle collisions are positively associated with fatalities, while the presence of pillion passengers and motorcycle-to-motorcycle crashes are negatively associated with fatalities. More importantly, in the specific context of Pakistan, morning hours, young riders, and female pillion passengers whose clothes stuck in the wheel significantly increase the fatal injury outcomes. Based on the findings, potential countermeasures to improve motorcycle safety are discussed, such as strict enforcement to control motorcyclists' risky behavior and speeding, provision of exclusive motorcycles lanes, and education of female pillion passengers. The findings from this study would increase awareness of motorcycle safety and can be used by the policymakers to enhance road safety in Pakistan, as well as in other developing countries with similar situations.

Prevalence of crashes and associated factors among commercial motorcycle riders in Bamenda, Cameroon

IntroductionCrash-related injury and fatality tends to be disproportionately higher among motorcycle riders compared to other types of motor vehicle users due to the absence of passive safety vehicle components of other vehicles. MethodsA population-based cross-sectional study among consenting commercial motorcycle riders conducted in Bamenda, Cameroon estimated the prevalence (commercial motorcycling lifetime and past 12-months) of self-reported crashes and examined contributing factors associated with crashes. ResultsAmong 552 participants, 77.4% self-reported a crash over lifetime as a commercial motorcycle rider. The prevalence of self-reported crash within the past 12 months was 21.5%. The odds of lifetime crash involvement were higher among riders with 3-4 years (Adjusted Odds Ratio [AOR] = 2.14; 95% CI = 1.14–4.01) or five years (AOR = 3.15; 95% CI = 1.73–5.73) of riding experience who typically carried more than two passengers (AOR = 3.57; 95% CI = 1.18–10.84). After adjusting for age, education level, marital status, and smoking status, riders were at increased odds of a self-reported severe crash of single-vehicle collision type if they typically used alcohol (AOR = 2.14, 95% CI = 1.22–3.76), rode on roads that were unpaved (AOR = 4.33, 95% CI = 2.50–7.52), muddy (AOR = 5.90, 95% CI = 1.60–21.73) or pothole-ridden (AOR = 2.41, 95% CI = 1.18–4.95), and at estimated speeds of 45 km/h or more (AOR = 2.55, 95%CI = 1.28–5.09). ConclusionOccurrences of road traffic crashes are frequent among commercial motorcycle riders who use alcohol, typically carry multiple passengers, speed, and ride on poor road conditions. Intervention efforts should focus on strategies to minimize crash-related injuries and fatalities by improving rider safety, infrastructure, and implementing effective road traffic policies.

Serious motor vehicle collisions involving young drivers on Norwegian roads 2013–2016: Speeding and driver-related errors are the main challenge

Objective: Motor vehicle collisions (MVCs) are a leading cause of death and acute disability among young adults worldwide. We performed a prospective study of young drivers involved in severe MVCs, investigating the critical events leading up to a collision with an emphasis on driver-related factors and collision culpability. Methods: A study was conducted in southeastern Norway of all drivers younger than 25 years who were involved in high-energy MVCs resulting in immediate hospitalization during 2013–2016. Collision investigators evaluated the exterior and interior of the motor vehicle (MV) within 24 h. Complementary information was obtained from interviews of collision victims, ambulance personnel and witnesses, from police reports, and medical records.Results: There were 145 young drivers included during a 3-year study period, representing an estimated incidence of 29 per 100,000 drivers with registered driving licenses. Ninety-two percent (133/145) were considered culpable of initiating the MVC, and only 2% of the critical factors preceding the collision were not related to the driver. There were 74% (108/145) males, the median MV (motor vehicle) age age was 14 years, and 86% (125/145) of the MVs were passenger cars. The MVCs predominantly occurred on rural roads (90%, 130/145). Among the culpable drivers, speeding behavior was the main predisposing factor in 80% (106/133) of the collisions. Driving at excessive speed was associated with single-vehicle collisions (87%, 74/85) and the presence of passengers (89%, 56/63). Compared to nonculpable drivers, culpable drivers were more often younger than 21years (66% vs 33%, p = 0.031), had obtained their license less than 2 years previously (68% vs 20%, p = 0.004), and were more likely to have been drinking or using drugs (27% vs 0%, p = 0.039). The overall rate of seatbelt use was 79% (114/145).Conclusion: The vast majority of injury-causing MVCs involving young drivers are initiated by those drivers. These incidents are characterized by male drivers with little driving experience who are operating old cars on rural roads at excessive speeds. Driving under the influence of alcohol or drugs is also not uncommon. These issues should be targeted in future preventive measures.

Differences in severity of injuries between motorcyclist and bicyclist fatalities in single vehicle collisions

To understand the injury severity of bicyclists and motorcyclists in single vehicle collisions, we performed a retrospective analysis of forensic autopsy cases of 25 motorcyclists and 14 bicyclists performed from 1999 to 2018. Collision details, injury characteristics, and involvement of alcohol were examined. The injury severity between bicyclists and motorcyclists was compared. The average age of victims was 62.3 years. All motorcyclists and no bicyclists wore a helmet. Motorcyclists had more severe injuries than bicyclists (mean Injury Severity Score of 42 and 27, respectively). The motorcyclists had more severe chest injuries but fewer severe facial injuries than bicyclists, owing to the difference in collision velocity or rate of helmet use (p < 0.05). Alcohol was present in the blood of 52.0% of bicyclists but no motorcyclists. The mean blood alcohol concentration of these bicyclists was 1.59 mg/mL. The bicyclists under the influence of alcohol had more severe injuries to neck and upper extremities than non-drunken bicyclists (p < 0.05). Our results may be useful for determining the cause of death and reconstructing the mechanisms of fatal injuries in bicyclists and motorcyclists.

Differences between occupational and non-occupational-related motor vehicle collisions in West Virginia: A cross-sectional and spatial analysis.

BackgroundMotor vehicle collisions comprise the majority of occupational-related fatalities in the United States and West Virginia has one of the highest occupational-related fatality rates in the nation. The purpose of this study was to compare work and non-work-related collisions, crash locations, and the characteristics of in-state and out-of-state drivers ≥18 years of age who were fatally injured in work-related collisions in West Virginia.MethodologyData were from the 2000–2017 Fatality Analysis Reporting System. Work and non-work-related crashes and characteristics in-state vs. out-of-state drivers were compared using binary and multivariable logistic regression analyses. Crash locations were compared via spatial analyses using kernel density estimations.ResultsAmong the 5,835 individuals fatally injured in crashes, 209 were designated ‘at work’. The odds of being a work-related crash were 85% lower [Odds Ratio (OR) = 0.15; 95% confidence interval (CI): 0.04, 0.49] among those testing positive for alcohol, but 2.5 times greater (OR = 2.56; 95% CI: 1.16, 5.65) among those holding a commercial driver’s license. The odds of being an in-state driver were 75% lower (OR = 0.25; 95% CI: 0.12, 0.53) among those wearing a safety belt, but 2.7 times greater among workers testing drug positive (OR = 2.67; 95% CI: 1.10, 6.52). In-state drivers were also less likely to be driving a large truck or be involved in single vehicle collisions and less likely to experience crashes on weekends, nights, or on highways. Spatial patterns of crash locations varied slightly between workers and non-workers.ConclusionsWork-related crashes differed greatly from non-work-related crashes in West Virginia. Stark differences existed between in-state and out-of-state workers and their crashes. Various avenues for workplace safety interventions exist, including seatbelt initiatives and drug testing policies for non-commercial drivers, which could help mitigate West Virginia’s elevated, occupational-related, traffic fatality rate.

Comparison of traffic collision victims between older and younger drivers in South Korea: Epidemiologic characteristics, risk factors and types of collisions.

ObjectiveThis study aimed to show the epidemiological characteristics and the difference in the risk factors and types of collision between older and younger drivers in Korea.MethodsWe collected data from the Emergency Department-based Injury In-depth Surveillance retrieved by the Korea Centers for Disease Control and Prevention from 2011 to 2015. We included injured drivers aged ≥ 18 years who were registered in the database, who were limited to drivers of four-wheeled vehicles. The enrolled patients were divided according to age into older (≥ 65 years) and younger (< 65 years) drivers. The total number of enrolled drivers was 37,511; 2,361 (6.3%) of them were older drivers. The epidemiological characteristics (e.g., age, sex, fatality rate) of traffic collision victims for 5 years were determined, and the risk factors (e.g., seat belt use) and types of collision (single- vs. multi-vehicle) between the two groups were compared.ResultsThe median age and interquartile range (IQR; 25th and 75th percentiles) of all drivers were 41.0 (IQR, 32.0–52.0), and 24,544 (65.4%) of them were men. The median age increased from 40.0 (IQR, 31.0–50.0) to 43.0 (IQR, 33.0–54.0) between 2010 and 2015 (P < 0.001). The proportion of older drivers increased from 5.0% to 8.4% annually during the study period (P < 0.001). Between 2010 and 2015, the fatality rate decreased from 3.1% to 1.2% (P = 0.287) for older drivers and from 0.9% to 0.5% (P = 0.009) for younger drivers. The proportion of single-vehicle collision (25.9% vs. 20.3%) was higher in older than in younger drivers (P < 0.001). Older drivers had a lower rate of seat-belt use than younger drivers (79.0% vs. 83.0%, P < 0.001).ConclusionsThe proportion of older drivers increased annually during the study period, and older drivers experienced more single-vehicle collision and used seat belt less frequently than younger drivers. A national policy support to reduce traffic collision in older drivers and public relation activities to enhance their seat belt use should be strengthened in the future.

Adaptation of a Canadian culpability scoring tool to Alberta police traffic collision report data

Objective: The objective of this study was to adapt a previously validated Canadian Culpability Scoring Tool (CCST) to Alberta police report data.Methods: Police traffic collision reports from motor vehicle (MV) collisions in Calgary and Edmonton (Alberta, Canada) from 2010 to 2014 were used. Adaptation of the CCST was completed with input from personnel within Alberta Transportation, contributing to face and content validity. Two research assistants, given only the information necessary for scoring, evaluated 175 randomly selected MV–MV collisions. Interrater agreement was estimated using kappa (k) and reported with 95% confidence intervals (CIs). Discussion of disagreements between the research assistants and consultation from Alberta Transportation informed the algorithm used in the Alberta Motor Vehicle Collision Culpability Tool (AMVCCT). The AMVCCT was automated and applied to all motorists involved in collisions. Binary logistic regression was used to examine characteristics of the culpable and nonculpable drivers and their effects were reported using odds ratios (ORs) with 95% CIs.Results: Interrater agreement for the random sample was excellent (k = 0.95; 95% CI, 0.92–0.99). Of those drivers hospitalized, 1,130 (37.54%) were rated not culpable and 1,880 (62.46%) were rated culpable. The odds of being culpable were higher for males than for females (OR = 1.43; 95% CI, 1.23–1.66). The odds of being culpable were higher in those impaired by alcohol than those considered “apparently normal” (OR = 61.10; 95% CI, 22.66–164.75). The odds of being deemed culpable, when compared with drivers >54 years old, were higher for those <25 years old (OR = 1.72; 95% CI, 1.35–2.20) and lower for those in the 40- to 54-year-old age group (OR = 0.78; 95% CI, 0.63–0.96). Driving between 12 a.m. and 6 a.m. resulted in higher odds of being culpable compare with all other 6-h time blocks. Direction and statistical significance remained consistent when applying the tool to all MV collisions. Sensitivity analysis including the removal of single vehicle collisions did not affect the direction or statistical significance of the main results.Conclusions: The AMVCCT identified a culpable group that exhibited characteristics expected in drivers who are at fault in collisions. The age groups 25–39 and 40–54 demonstrated different results than the CCST. However, this is the only difference that exists in the findings of the AMVCCT compared to the CCST and could exist due to differences between the driving populations in Alberta and British Columbia. It is possible to adapt the CCST to provinces outside British Columbia and, in doing so, we can identify risk factors for collision contribution and not-at-fault drivers who represent the driving population.

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergency-related announcements is approximately 7 s.

Using random-parameter and fixed-parameter ordered models to explore temporal stability in factors affecting drivers' injury severity in single-vehicle collisions

Abstract Understanding the temporal stability in the factors influencing drivers’ injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that researchers could understand whether any safety improvements, observed after applying a certain safety treatment, are attributed to the specific treatment or simply attributed to the temporal instability of the factors being addressed. This study investigates the temporal stability of the factors affecting drivers’ injury severity in single-vehicle collisions involving light-duty vehicles. The study is based on utilizing ordinal regression modeling to analyze the severity of drivers’ injuries in all police-reported light-duty single-vehicle collisions that occurred in North Carolina from January 1, 2007, to December 31, 2013. A separate regression model was estimated for each year so that statistical significance of each risk factor may be compared over the years. The study also estimated random-parameter (mixed) ordered logit models to explore the heterogeneity in data. The most significant factor that was found to increase the severity of drivers’ injuries in light-duty single-vehicle collisions is driving under the influence of alcohol or illicit drugs. Other significant factors, in decreasing order in terms of their significance, include driving on a highway curve, exceeding speed limit, lighting conditions, the age of the driver, and the age of the vehicle. In contrast, there were six factors that were found to be significant in only some years and not in all years. These six temporally unstable factors include the use of seatbelt, driver’s gender, rural highways, undivided highways, the type of the light-duty vehicle, and weather and road surface conditions. These same factors were found by other previous research studies to be significant and stable predictors of drivers’ injury severity in single-vehicle collisions.

Method to Improve the Positioning Accuracy of Vehicular Nodes Using IEEE 802.11p Protocol

Currently, vehicle incidence remains high, but the related research is mainly focused on single-vehicle collision warning, which is unable to notice the following multiple endangered vehicles in emergency. The main technical challenge of chain collision warning is accurate and seamless ranging or positioning of the neighboring multiple vehicles. Because of the non-line of sight (NLOS) environments, the traditional ultrasonic or laser method does not work. At the same time, the accuracy of global navigation satellite system (GNSS) is over 10 m, such as global positioning system (GPS) or BeiDou satellite positioning system (BDS), which is not efficient for the cooperative collision avoidance (CCA) system. Moreover, GNSS fails to operate in NLOS tunnels or downtown areas where blockage of satellite signals is frequent. Thus, in this paper, a seamless and high accuracy positioning method based-on three kind of multi-source information fusion is proposed, i.e., the high accuracy local positioning information is provided by IEEE 802.11p protocol, the dual-mode wide area differential positioning information provided by the fusion of BDS and GPS, the positioning information of dead reckoning provided by the fusion of on-board diagnostic (OBD) and micro-electro-mechanical system-inertial navigation system. In vehicle positioning system, the accurate time of arrival (TOA) estimation is very important, so in this paper, a two steps method for TOA estimation using the IEEE 802.11p short preamble is proposed. Simulations show that in both the international telecommunication union vehicular multipath channel and the additive white Gaussian noise channel, the proposed method provides better accuracy and is less time complex than some well-known methods. The estimated TOA fused with the GNSS and OBD information can be used for seamless and high accuracy positioning in the CCA system to avoid traffic accident.