Crash Types Research Articles

A Review of the Accuracy of Crash Coding When Applied to Motorcycle Crashes

The coding of road crashes is designed to identify patterns of contributing factors for the development of countermeasures. In Australia, crash type is defined by the last vehicle movement before the first crash impact. This coding system was originally based on police data for motor vehicle crashes but did not distinguish between vehicle types. The aim of this study was to examine the accuracy of current crash coding practice when applied to motorcycle crashes. This was a population-based study of 1,479 motorcycle crashes reported in Tasmania between 2013-2016. All crashes were recoded based on details from police reports and the revised codes were compared to those assigned by the road authority. The defining role of the last vehicle movement was suspended in cases where it excluded other prior contributing factors identified in the police report. Almost half of the motorcycle crashes were reclassified to incorporate additional information from the police reports including contributing factors or prior events that are characteristic of motorcycle crashes. These factors had been excluded in the original coding due to the last vehicle movement protocol. The last vehicle movement is not an appropriate method for classifying motorcycle crashes. Training in crash reporting for police and road authorities should include awareness of the different contributing crash factors for different vehicle types and the importance of accurate crash data for the development of targeted countermeasures. The addition of motorcycle specific crash categories and subcodes to the coding systems is also recommended.

Analyzing Autonomous Vehicle Collision Types to Support Sustainable Transportation Systems: A Machine Learning and Association Rules Approach

The increasing presence of autonomous vehicles (AVs) in transportation, driven by advances in AI and robotics, requires a strong focus on safety in mixed-traffic environments to promote sustainable transportation systems. This study analyzes AV crashes in California using advanced machine learning to identify patterns among various crash factors. The main objective is to explore AV crash mechanisms by extracting association rules and developing a decision tree model to understand interactions between pre-crash conditions, driving states, crash types, severity, locations, and other variables. A multi-faceted approach, including statistical analysis, data mining, and machine learning, was used to model crash types. The SMOTE method addressed data imbalance, with models like CART, Apriori, RF, XGB, SHAP, and Pearson’s test applied for analysis. Findings reveal that rear-end crashes are the most common, making up over 50% of incidents. Side crashes at night are also frequent, while angular and head-on crashes tend to be more severe. The study identifies high-risk locations, such as complex unsignalized intersections, and highlights the need for improved AV sensor technology, AV–infrastructure coordination, and driver training. Technological advancements like V2V and V2I communication are suggested to significantly reduce the number and severity of specific types of crashes, thereby enhancing the overall safety and sustainability of transportation systems.

Analysing crash severity on expressways in India using statistical and ML approaches

The high injury severity of traffic crashes on Indian expressways is a significant concern for road safety experts, although studies dedicated to this critical issue are limited. The present study investigates the factors influencing crash severity using multinomial logit (MNL), decision tree (DT) and random forest (RF) models on a dataset of 2,747 crashes on three selected expressways. The dependent variable, crash severity, had four injury severity categories: fatal, severe, minor, and property damage only (PDO). Various explanatory variables, included traffic and speed characteristics, temporal and geometric characteristics, primary contributing factors, crash type, and the vehicles involved. Synthetic minority oversampling (SMOTE) and randomise class balancing (RCB) techniques were also employed to tackle the class imbalance issue in the dataset. The predictive performance of models was evaluated using classification accuracy and Kappa value. The RF model showed the highest predictive accuracy on the RCB dataset. The key findings highlight the critical need for enforcement of speed limits and entry restrictions, lane discipline, and improvement of design deficiencies such as provisions of truck laybys and bus bays. These measures can help in policy-making and engineering improvements to enhance road safety on expressways in India.

Risk factors for bicycle-related overtaking, rear-end, and door crashes in the United Kingdom

Abstract Background Prior studies have offered valuable insights into risk factors associated with bicycle crashes at intersections. Nevertheless, limited research focuses on investigating three types of bicycle crashes that commonly occur on road segments: overtaking, rear-end, and door crashes. Methods In the present research, risk factors for these three types of crashes on road segments were examined by analyzing British STATS19 reports of crashes from 1991 to 2020. We calculated adjusted odds ratios (AORs) with 95% confidence intervals (CIs) for multiple risk factors using multivariate logistic regression models. The analysis included 127,637 bicycle crashes, categorized into 18,350 overtaking, 44,962 rear-end, 6,363 door, and 57,962 other crashes. Results Speed limits of ≥ 40 miles per hour (mph) (AOR = 2.238, 95% CI = 2.159-2.320), heavy goods vehicles (HGVs) as crash partners (AOR = 2.867, 95% CI 2.473-3.323), and elderly crash partners (AOR = 2.013, 95% CI = 1.937-2.092) were significant risk factors for overtaking crashes. Notable risk factors for rear-end crashes included midnight hours (AOR = 1.269, 95% CI = 1.190-1.354) and unlit darkness (AOR = 1.486, 95% CI = 1.404-1.573). Speed limits 20-30 mph (AOR = 16.185, 95% CI = 13.514-19.382), as well as taxis and private rental automobiles (AOR = 2.695, 95% CI = 2.310-3.145), were factors associated with door crashes. Further intriguing findings from our joint-effect analysis included higher probabilities for overtaking crashes in rural regions when older drivers and HGVs were crash partners (AOR = 2.93, 95% CI = 2.79-3.08 and AOR = 2.62, 95% CI = 2.46-2.78). Conclusions The mentioned risk factors have remained mostly consistent since our previous study was conducted in 2011. However, current research elucidated that certain factors have become more significant under specific circumstances. For instance, in rural areas, cyclists were more likely to be involved in overtaking crashes with heavy goods vehicles as crash partners. Key messages • Bicycle crash; Road segment; Door crash; Overtaking crash; Rear-end crash

Relationship between urban traffic crashes and temporal/meteorological conditions: understanding and predicting the effects

Urban traffic accidents pose significant challenges to public safety and transportation management. Previous studies have revealed that temporal and meteorological factors are the key contributors to accident rate. Besides the inconsistent observations or lack of exploration in some aspects such as snowfall, fog, wind and daily temperatures, it has been shown that these factors are essentially entangled. Furthermore, existing methodologies of analysis or prediction have been limited to relative risk or traditional models. Hence, this study is centered on understanding the detailed correlations between temporal and meteorological factors and accident rate of two types of crashes – moving vehicle and fixed-object crashes using the traffic accident data from Dalian. Further, by incorporating a diverse set of the features, a prediction model leveraging the random forest algorithm is proposed and proved effective in anticipating accident occurrences on the district level. The feature importance analysis has shown that time period and factors such as holiday, temperature and season are most important factors. The rate is higher on working days and in spring, and collisions of both types peak at 6–7 am. When the highest daily temperature is 27 °C and the lowest is 20 °C or -8 °C, the incidence is relatively higher. On the basis, the recommendations are aimed at optimizing transportation systems, mitigating accident risks, and enhancing public safety in urban environments.

Prehospital factors associated with mortality among road traffic injury patients: analysis of Cameroon trauma registry data

BackgroundCameroon is amongst the worst affected countries by road traffic injuries with an estimated 1443 disability-adjusted life years per 100,000 population. There have been very limited reports on the crucial prehospital response to road traffic injuries in Cameroon. This study aimed to identify prehospital factors associated with RTI mortality in Cameroon.MethodsWe included patients enrolled between June 2022 and March 2023 in the Cameroon Trauma Registry. Information about prehospital factors and demographic data was obtained from patients or their proxies. We examined the association of prehospital care factors like care at the crash scene and type of transportation during crash with final patient outcome. We used Chi-squared test to investigate the association between selected independent variables and mortality. A multivariable logistic regression model was built to identify independent predictors of dying from an RTI.ResultsRTIs constituted 69.5% (n = 3203) of all injuries in the Cameroon Trauma Registry. Only 20.7% (n = 102) of 4 + wheel vehicle occupants had seatbelts on and just 2.7% (n = 53) of motorcycle riders were wearing helmets during the collision. Only 4.9% (n = 156) of patients received any form of scene care. In-hospital mortality was 4.3% (n = 139) and was associated with male sex (AOR = 1.7, 95%CI = 1.08–2.80), crashing on a motorcycle (AOR = 2.08, 95%CI = 1.1–3.67) and scene care (AOR = 0.25, 95%CI = 0.04–0.80).ConclusionsReceiving any type of care at the scene such as bleeding control or being placed in the recovery position by bystanders is associated with improved survival. Improving on existing informal prehospital care responses should be a priority in Cameroon.

Factors Affecting Single and Multivehicle Motorcycle Crashes: Insights from Day and Night Analysis Using XGBoost-SHAP Algorithm

This study aimed to identify and compare the risk factors associated with motorcycle crash severity during both daytime and nighttime, for single and multivehicle incidents in Thailand using 2021–2024 data. The research employed the XGBoost (Extreme Gradient Boosting) method for statistical analysis and extensively examined the temporal instability of risk factors. The results highlight the importance of features impacting the injury severity of roadway collisions across various conditions. For single motorcycle crashes, the key risk factors included speeding, early morning incidents, off-road events, and long holidays. In multivehicle crashes, rear-end collisions, interactions with large vehicles, and collisions involving other motorcycles or passenger cars were linked to increased injury severity. The findings indicate that the important factors associated with motorcyclist injury severity in roadway crashes vary depending on the type of crash and time of day. These insights are valuable for policymakers and relevant authorities in developing targeted interventions to enhance road safety and mitigate the incidence of severe and fatal motorcycle crashes.

Evaluation of SSF, Roof Crush Test as an Alternative to Dynamic Rollover Test using Numerical Approach

Rollover accidents are classified into various scenarios, and with the increasing prevalence of high-centre-of-gravity (CG) vehicles in Southeast Asia (SEA) particularly in Malaysia, such as SUVs, MPVs and pickup trucks; assessing vehicle rollover risk has become critical. Even though rollover incident recorded low number of cases compared to other crash types in Malaysia, it had the highest casualty index for killed or seriously injured (KSI) and the situation induces worries because majority of the fatalities in rollover crashes among passenger vehicle occupants goes to high CG vehicle type. There are several limitations from the SEA nations to enforce costly dynamic rollover test as safety specification for SEA market requirement. Therefore, this study aims to evaluate Static Stability Factor (SSF), and roof resistance test (FMVSS 216), as an alternative to the dolly rollover test (FMVSS 208) using Finite Element Analysis (FEA). A correlated Chevrolet Silverado MY 2014 model is employed, with different SSF values representing varying levels of vehicle stability. The study evaluates peak force, peak energy absorption, and maximum intrusion during rollover simulations according to FMVSS 208 for vehicle models with different SSF values. Results indicated that vehicles with lower SSF values lead to higher energy absorption and intrusion, signifying increased rollover impact. Additionally, the article suggests that FMVSS 216 Roof Resistance Test is more severe than FMVSS 208 and vehicle rating system based on SSF value is vital for market awareness. Understanding the vehicle structural integration in rollover scenarios is essential for improving safety measures and vehicle design, especially for high-centre-of-gravity vehicles prevalent in the market.

Enhancing road safety with the infrastructure-adaptable NDBA 2.0 concrete median barrier: An Italian experience

Road safety is a crucial global concern because of the high number of fatalities and injuries resulting from road crashes each year. Median crossover collisions are among the most dangerous crashes that happen on highways, frequently leading to serious or fatal injuries. The main approach to decreasing the occurrence of these types of crashes is the installation of median barriers. When the need for such installations arises, road agencies must choose from various options, including concrete barriers, cable barriers, or metal-beam guardrails. This paper is dedicated to the New Dynamic Barrier for Highways (NDBA 2.0), an innovative technology for median barriers developed by the Italian National Road Agency (ANAS), emphasizing its pivotal role in enhancing road safety. It incorporates high-tensile steel and advanced composites, offering robust protection while maintaining a lightweight profile. What distinguishes the NDBA 2.0 is its dynamic nature, featuring an intelligent system that seamlessly adapts to the road infrastructure. Its modular construction, with sections of only 200 cm, allows for easy installation and ensures compatibility across successive road segments. This adaptability reduces construction time while maintaining the highest standards of performance. From a road safety perspective, the NDBA 2.0 offers substantial advantages. Its design contributes to minimizing crash-related costs by reducing the severity of crashes, particularly in the transition zones. The barrier's design allows it to adapt to varying road conditions and traffic volumes, effectively addressing common installation challenges on existing roadways as well. Its ability to be directly supported on the road surface wear layer eliminates the need for costly foundation structures, facilitating quick installation and reducing maintenance expenses. The NDBA 2.0 barrier was designed to eliminate the need for future simulations in the design and verification of transitions between different barriers. For this reason, the NDBA 2.0 barrier has been tested in real-world conditions in class H4 and, consequently, is equipped with CE marking. This study offers a comprehensive analysis of the NDBA 2.0 barrier, whose implementation may provide significant benefits for road safety. Continued research, collaboration, and widespread adoption of the NDBA 2.0 barrier can further enhance road safety on a global scale.

Effects of seatback angle, seat rotation, and impact speed on injury risk of occupants in highly automated vehicles in frontal crashes

Objective The objective of this study is to examine the effects of seatback angle, seat rotation, and impact speed on occupant kinematics and injury risk in highly automated vehicles. Methods The study utilized the Global Human Body Models Consortium midsize male (M50-OS+B) simplified occupant model in a simplified vehicle model (SVM) to simulate frontal crashes. The M50-OS+B model was gravity-settled and belted into the driver and left rear passenger seat. To investigate the effects of seatback angle, seat rotation, and impact speed on occupant kinematics and injury risk in frontal crashes, a design of experiments (DOE) was conducted. The DOE incorporated four seatback angles (13°, 23°, 45°, and 57.5° about vertical), four seat rotation angles (0°, 25°, 45°, and 90°), three impact speeds (25, 35, and 45 kph), and four frontal crash type configurations. All four seatback angles were used with 0° seat rotation, whereas 13° seatback angle was used with the remaining seat rotation configurations because of cabin fit considerations. Injury risks were estimated for the head, neck, shoulder, thorax, pelvis, and lower extremities for both occupants for each simulation (n=588). Results Statistically significant differences between all the groups within each independent variable category were observed based on the analysis of variance. HIC-based head injury risk and chest injury risk decreased and femur force for the driver and tibia force for the passenger increased with an increase in seatback angles. The head injury risk increased with seat rotation. All the injury risks increased with an increase in impact speed. The driver airbag was able to safeguard the driver from head injuries for all seat rotations except at 90° of seat rotation. Conclusion This is the first vehicle modeling study that collectively looked at the effects of seatback angle, seat rotation, and impact speed along with the interaction of occupants on the risk of injury in frontal crashes. The rear passenger experienced higher seatbelt loads than the driver. More reclined seats decreased head and chest injury risk, but increased driver femur injury risk and rear passenger tibia injury risk. Results underscore the necessity for additional anti-submarining mechanisms and driver airbag designs adapted for the anticipated occupant positions.

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000–10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Exploration of the Characteristics of Elderly-Driver-Involved Single-Vehicle Hit-Fixed-Object Crashes in Pennsylvania, USA

With the acceleration of population aging, the elderly driving safety issue is increasingly prominent. Method: With the crash data of Pennsylvania from 2010 to 2019, this study exclusively discusses features of single-vehicle hit-fixed-object crashes (SVHFOCs), one of the most common and deadliest crash types for elderly drivers. Results: Firstly, we demonstrate that elderly drivers are much more likely to be injured and killed than young drivers in SVHFOCs by checking crash consequences. The descriptive analysis indicates that elderly drivers have very different crash features from young drivers. They are found to drive with more caution in many aspects, such as more low-speed local travels, fewer illegal behaviors, fewer nighttime travels, etc. Then, a logistic regression model is built to find the factors significantly influencing the severity of SVHFOCs from driver, vehicle, roadway, and environment. The estimation results indicate that female sex, not wearing a seatbelt, DUI, rural area, and SUV involvement tend to be associated with more severe SVHFOCs. Additionally, illumination, weather, and road type could also significantly affect crash severity. Especially, SVHFOCs in adverse weather, in dark conditions, and at intersections are found to be less severe, which implies that elderly drivers might drive more carefully in complex environments. Practical Applications: These findings are expected to provide new insights for agencies in formulating customized measures to prevent elderly drivers from being involved in SVHFOCs.

A comparison between bridge-related and non-bridge crashes: severity, vehicle involvement, and motorists’ characteristics

Objectives Bridges, though representing a small portion of transport networks by length, are still prone to traffic crashes. Despite extensive research on bridge-related crashes globally, there’s a scarcity of studies exploring differences between bridge- and non-bridge-related crashes. Thus, this paper attempts to add to the current knowledge by comparing bridge-related crashes and non-bridge crashes using a case study in Victoria, Australia. Methods By utilizing crash data in Victoria between January 2006 and April 2023; the Chi-squared test is conducted to examine differences between crash types. Partial proportion odds models are then employed to establish if bridge-related crashes tend to be more severe. Results In general, the analysis reveals that bridge-related crashes tend to result in more severe injury outcomes compared to non-bridge crashes. Furthermore, there is evidence that bridge-related crashes are more likely to be single-vehicle, to involve a heavy vehicle as well as young/male motorists, and to occur on high-speed roads, regional areas and at nighttime than non-bridge crashes. Conclusions A targeted program aiming at enhancing traffic safety at bridges is recommended due to the higher severity of bridge-related crashes. Considering the presence of bridges on the road network, and particularly in regional areas where many are in poorer conditions, it is essential to consider retrofitting as well as low-cost solutions in order to complement motorist education and awareness programs. Specific groups with higher risks of being involved in bridge-related crashes, such as young male motorists, should be targeted in these programs.

Investigating the Interrelationships among Factors Associated with Automated Vehicle Crashes Using Additive Bayesian Network

Although automated vehicles (AVs) were considered a promising solution to enhance traffic safety by eliminating human errors, AV crashes still happen in mixed traffic consisting of human-driven vehicles and AVs. Thus, to reduce AV-involved crashes, it is necessary to understand the factors leading to AV crashes. However, traditional regression-based methods may not reveal a structured relationship among leading factors of AV crashes, which hinders the exploration of countermeasures to AV crashes. Based on the 246 AV crash records collected by the National Highway Traffic Safety Administration, this study investigated the factors associated with AV crashes. An additive Bayesian network (ABN) approach was utilized to construct the topological relationship among potential influential factors of AV crashes, followed by post-ABN regression analyses. Results show that, though AV technologies have developed rapidly in the past few years, rear-end crashes are still dominant among AV-involved crashes, potentially because of the discrepancy in the driving behaviors between AV and human-driven vehicles. The crash type of AV-involved crashes is more related to the pre-crash movements of crash partners than it is to the pre-crash movements of AVs, while crash outcomes (e.g., injury severity) are associated with the environmental factors (e.g., operating entities) and crash-procedure-related factors (e.g., crash type). Findings from this study aid in understanding AV crash patterns, which can inform targeted interventions and technology advancements to improve safety outcomes for all road users.

A mixed-methods examination of fixed-object crashes among electric utility company fleet vehicles

IntroductionOne of the most common and serious types of crashes among all motor-vehicle users involves collisions with fixed objects. This type of crash occurs frequently among utility vehicle workers while driving for work. The overarching objective of this research was to improve the safety of electric utility company vehicle operators by determining the circumstances under which utility vehicles are involved in crashes with fixed objects and to provide recommendations to help drivers and utility fleet company management prevent these types of crashes. MethodsThe study incorporated a mixed methods approach and gathered information from structured interviews with safety managers at electric power companies and an analysis of utility vehicle crashes using data supplied by electric power companies and a statewide database in Michigan. ResultsMany factors were found to contribute to fixed-object crashes including engagement in secondary tasks and job productivity pressure. Information was organized and evaluated to develop 11 recommendations for countermeasure strategies that are directed at various levels within the system in which the drivers operate. DiscussionThis study was the first to explore fixed-object crashes among electric utility fleet vehicles. The findings are unique in that they provide insight into the safety practices in an industry that has received limited academic attention and highlight the need to place greater emphasis on road safety practices in the workplace. Practical applicationCountermeasures generated for this study are applicable to companies in which fleet driver safety is a primary concern.

Descriptive analysis of road traffic crashes encountered by Tanzanian motorcycle taxi drivers trained in first aid

IntroductionIn Dar es Salaam, Tanzania, death from road traffic crashes (RTC) occurs at roughly double the global rate. In this study, we sought to understand the locations and types of vehicles involved in RTC in Dar es Salaam encountered by a cohort of motorcycle taxi drivers previously trained in first aid. MethodsThis was a quasi-non-randomized interventional study, cohort subtype, with three-month follow-up. Some 186 motorcycle taxi drivers were selected by convenience sampling from 16 heavily populated, central wards and trained in a basic hemorrhage control course. Participants reported the location and types of vehicles involved in RTCs they encountered and intervened upon through performing bleeding control interventions. Surveys were designed on KoboToolbox and administered via phone call at monthly intervals over a three-month period. The main outcome measures were the location of crash encounters and types of vehicles involved. ResultsIn all 62 unique participants (33.3 %) encountered and provided bleeding control interventions to 83 injured individuals following 69 RTC in at least 31 distinct city wards, despite training only having occurred in 16 wards. Eight crash locations were not recorded. Crashes in distant wards typically contained major roads. Most commonly, crashes involved a motorcycle without the involvement of another vehicle (n=20), followed by motorcycle vs. car/three-wheeled vehicle (n=15), motorcycle vs. bus/van (n=10), motorcycle vs. motorcycle (n=9), motorcycle vs. pedestrian (n=7), pedestrian vs. bus/van (n=2), pedestrian vs. car/three-wheeled vehicle (n=1), motorcycle vs. bicycle (n=1), multi vehicle (n=1), and other (n=3). ConclusionsMotorcycle taxi drivers trained in hemorrhage control frequently encounter and intervene upon RTC in wards where they are based as well as in distant locations, commonly in wards containing major roads. Expanding first aid training for motorcycle taxi drivers could improve timely access to emergency care for RTC victims. Since most crashes involved motorcycles, road safety training should be integrated into future courses.

Effect of ESC (electronic stability control) on tree and pole impacts with focus on rear impacts

ObjectiveThe effect of ESC (Electronic Stability Control) was investigated for the rate of crash exposure, serious injury and fatality in pole and tree impacts. Field data was analyzed by crash type (front, side, rear and rollover) and model year (MY) before, during and after the implementation of ESC. MethodsThe number of pole and tree impacts was determined for four groups of vehicle model years (MY): 1981–1989 MY and 1990–2002 MY before the introduction of ESC, 2003–2009 during the phase-in of ESC and 2010–2020 MY after essentially all vehicles were equipped with ESC. Collisions were grouped by front, side, rear and rollover. Three databases were analyzed: 1990–2020 FARS, 1990–2015 NASS-CDS and 2017–2020 CISS. Vehicle registration was obtained from IHS Markit to determine the rate of pole and tree impacts per 100,000 registered vehicles. The same vehicle selection criteria was used for vehicle registration and crash data. ResultsFatalities dropped 65.2 % (95 % CI, 63.0–67.4 %), z = 43.7, p < 0.001 into poles and 60.3 % (95 % CI, 59.0–61.5 %), z = 72.4, p < 0.001 into trees in vehicles equipped with ESC comparing 1990–2002 MY to 2010–2020 MY vehicles. Seriously injured occupants in crashes with poles dropped 75.9 % (95 % CI, 75.0–76.9 %), z = 116, p < 0.001 between 1990 and 2002 MY and 2010–2020 MY vehicles. There was a 65.2 % (95 % CI, 64.4–65.9 %), z = 141, p < 0.001 reduction in tree impacts. The crash exposure to pole impacts dropped 36.0 % (95 % CI, 35.8–36.3 %), z = 252, p < 0.001 from 80.77/100,000 registered vehicles in 1990–2002 MY vehicles to 51.69/100,000 in 2010–2020 MY vehicles. There was a 61.0 % (95 % CI, 60.8–61.2 %), z = 434, p < 0.001 reduction in tree impacts. For rear impacts, fatalities dropped 82.9 % (95 % CI, 71.3–94.4 %), z = 9.37, p < 0.001 into poles and 74.8 % (95 % CI, 67.8–81.9 %), z = 14.8, p < 0.001 into trees. Serious-injury in rear impacts with poles and trees were essentially eliminated in 2010–2020 MY vehicle crashes. There were significant drops in fatalities in side and frontals impacts and rollovers in vehicles equipped with ESC. ConclusionESC helps the driver maintain vehicle heading and significantly reduced the rate of serious injury and fatality in off-road impacts with poles and trees. The benefits of ESC may not be realized with impairments when the driver does not appropriately steer the vehicle.

A cross-sectional study of the prevalence of medical conditions as contributors to road crashes in South Australia

Objectives With the population of Australians aged 85 or older expected to double by 2042 and the association of increased age with the greater likelihood of having medical conditions, the potential for an increase in the number crashes caused by medical conditions is of concern. This study aims to understand the prevalence of medical conditions contributing to road crashes and the characteristics of these crashes, in order to guide development of strategies to mitigate the risks and harms involved. Method The sample consisted of 1,934 active road users (drivers, motorcyclists, cyclists, pedestrians) injured in crashes on South Australian roads who were admitted to the Royal Adelaide Hospital over the period 2014 to 2017. Results The findings indicate that 9.2% of hospital admission cases were directly caused by a medical condition or acute medical event, with loss of consciousness, attempted suicide, and seizures the most commonly observed. Crashes attributed to a medical condition were more likely to involve older drivers and those with a greater number of preexisting medical conditions. Conclusion Medical conditions or acute medical events are a significant contributor to road crashes. Common crash types involving medical conditions or events – single vehicle, hit object – indicate that vehicle technologies such as lane keeping assist and autonomous emergency braking, and infrastructure treatments such as roadside barriers, may help to prevent crashes in which a driver experiences a medical event. Monitoring of drivers with medical conditions should continue, while the benefits of social policies that reduce reliance on private motor vehicles for transport should also be explored.

Enhancing Road Safety Strategies through Applying Combined Treatments for Different Crash Severity

This research examines the utility of combined crash modification factors (CMFs) in minimizing crash severity at urban roundabouts. Conventional CMFs typically assess the influence of singular interventions on road safety. However, traffic dynamics and the interactions among multiple safety measures necessitate a more comprehensive strategy. This study introduces a novel methodology for developing integrated CMFs that accounts for the interactive effects of multiple safety measures, providing a comprehensive understanding of their collective impact on road safety. The investigation utilized data from 16 urban roundabouts in Amman, Jordan, from 2015 to 2021. Regression models have been employed to develop individual and combined CMFs for various geometric and traffic characteristics, such as entry and exit widths, speed limits, and weaving patterns. Key findings indicate that interventions, such as reducing speed limits, modifying entry and exit widths, and adjusting weaving lengths, significantly improve safety. The analysis identifies hazardous roundabouts and proposes effective single and combined treatments to mitigate crash risks. This study highlights the importance of considering multiple treatments simultaneously to improve the predictive accuracy of safety assessments and supports the development of more effective road safety strategies tailored to specific crash types and severities. The approach demonstrates a significant potential to enhance road safety analysis and decision-making, ultimately contributing to safer road environments.

Identification of Risk Factors for Severe Younger and Older Driver Crashes in Massachusetts

Systemic safety analysis is a comprehensive and proactive approach to identifying and addressing high-risk roadway features and crash types that contribute to severe crashes. The Massachusetts Department of Transportation identified 14 emphasis areas for systemic improvement, including younger and older driver crashes. Research was undertaken to conduct separate comprehensive analyses of fatal and serious injury crashes involving younger and older drivers, using a systemic safety approach to develop risk factors and identify critical locations within the state. The study utilized crash, roadway, driver’s license, citation, healthy aging, and environmental justice data from various sources. Negative binomial regression models were developed to relate crash frequency with town-specific traffic, behavior, and site characteristics, considering exposure and spatial variance. The findings highlighted the significance of multiple risk factors contributing to severe crash frequency. For younger drivers, the presence of young drivers in the town, risk-taking behaviors such as unbelted and impaired driving, and infrastructure characteristics, particularly high-speed facilities and historically underinvested infrastructure, were key contributors to crash frequency. Older drivers’ crash frequency was influenced by the presence of older drivers, healthy aging indicators, risk-taking behaviors, and high-speed facilities. Based on the model results, risk scores were developed for each variable in the model and applied to each town. Towns were ranked using percentile ranking to classify them into primary and secondary risk categories. The towns identified in the primary risk category (18 towns in the top 5 percentiles) captured approximately 13.5% of severe younger driver crashes and 14.5% of severe older driver crashes.