Modeling and comparing injury severity of at-fault and not at-fault drivers in crashes

Young novice drivers' road crash injuries and contributing factors: A crash data investigation

OBJECTIVE

Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers' crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures.

METHODS

Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022.

RESULTS

The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level).

CONCLUSIONS

The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.

Aberrant behaviors of drivers involved in crashes and related injury severity: Are there variations between the major cities in the same country?

INTRODUCTION

Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved.

METHOD

In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions). Two other models were built to study the influence of the same factors on the injury severity of the occupants of vehicles for which crash circumstances related to driver aberrant behaviors were observed and of the involved pedestrians. The variability between the 10 different cities was considered through a multilevel approach, which revealed a significant variability only for the inattention-related crash circumstance. In the other models, the variability between cities was not significant, indicating quite homogeneous results within the same country.

RESULTS

The results show several relationships between crash factors (driver, vehicle or road-related) and human-related crash circumstances and severity. Unsignalized intersections were particularly related to the illegal maneuvering crash circumstance, while the night period was clearly related to the speeding-related crash circumstance and to injuries/casualties of vehicle occupants. Cyclists and motorcyclists were shown to suffer more injuries/casualties than car occupants, while the latter were generally those exhibiting more aberrant behaviors. Pedestrian casualties were associated with arterial roads, heavy vehicles, and older pedestrians.

Interpretable machine learning for evaluating risk factors of freeway crash severity

Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is crucial for comprehending ML results and aiding informed decision-making. This study aims to implement an interpretable ML to visualize the impacts of factors on crash severity using 5 years of freeways data from Iran. Methods including classification and regression trees (CART), K-nearest neighbours (KNNs), random forest (RF), artificial neural network (ANN) and support vector machines (SVM) were applied, with RF demonstrating superior accuracy, recall, F1-score and ROC. The accumulated local effects (ALE) were utilized for interpretation. Findings suggest that light traffic conditions (volume / capacity < 0.5 ) with critical values around 0.05 or 0.38, and higher proportion of large trucks and buses, particularly at 10% and 4%, are associated with severe crashes. Additionally, speeds exceeding 90 km/h, drivers younger than 30 years, rollover crashes, collisions with fixed objects and barriers, nighttime driving and driver fatigue elevate the likelihood of severe crashes.

Exploring the differences in rider injury severity in vehicle-two-wheelers accidents with dissimilar fault parties

Objective: The division of responsibility in vehicle-two-wheelers accidents reflects the extent to which different fault parties contributed to the occurrence of the accident, with significant differences in the injuries sustained by the riders in accidents where diverse parties were primarily responsible. We want to explore the difference in the severity of injury of riders in different fault parties of accidents so that we can make targeted protection improvements.Methods: In this study, three generalized ordered logit models were established for the total sample (n = 1204), the sample with drivers as the primary fault party (n = 607), and the sample with riders as the primary fault party (n = 597), respectively, to explore the differential impact factors on rider injury severity in vehicle-two-wheelers accidents involving different fault parties. Inter-group difference tests were conducted on the mean rider injury severity caused by differential factors in different accidents. Combining the impact effect trends and mean differences in the model, the differences in rider injury severity in accidents involving different fault parties were analyzed from the standpoints of human, vehicle, and road factors.Results: It was found that the effects of curve on injury severity was sheerly opposite in accidents with different fault parties and that factors, such as visual obstruction, road surface condition, gender, and helmet wearing differed in their effects on rider injury severity under different fault parties accidents. This reveals the driving tendencies and states of both parties in different environments.Conclusion: Based on the differential impact factor analysis and rider injury characteristics in accidents involving different fault parties, suggestions for improvement were made from the perspectives of road facilities, and safety awareness of drivers and riders, which are beneficial for improving rider safety and providing a theoretical reference for future regulations on liability allocation.

Characterizing the effects of contributing factors on crash severity involving e-bicycles: a study based on police-reported data

Mitigating e-bicycle crash occurrence has become a great challenge across the world. It is of paramount importance for improving traffic safety to characterize the relationship between e-bicycle crash injury severities and contributing factors. This study positions itself at clarifying the roles of the factors in e-bicycle crashes from time, space, road, environment, rider and object characteristics. The partial proportional odds (PPOs) model as well as its elasticity analysis was employed to identify the influences based on 15,138 police-reported e-bicycle crashes in Shangyu District of Shaoxin City, China. The results evidenced that there were 12 factors having significant effects. Especially, the results emphasized the greater influences of rider gender, age, object hit and road type. Their maximum of the absolutes of elasticities was greater than 24%. Increased crash severity was associated with females, younger riders, and higher speed collisions. However, the remaining significant variables had minor effects (no more than 10%). The findings provide meaningful insights for advancing e-bicycle development, when making related policies and prioritizing safety countermeasures.

Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile

Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

Factors associated with consecutive and non-consecutive crashes on freeways: A two-level logistic modeling approach

A consecutive crash consists of a primary crash and one or more secondary crashes that occur subsequently in a short period of time within a certain distance. It often affects a relatively large area of road space and the traffic disruption created can be difficult for traffic managers to control and resolve. This study identifies the factors delineating a primary crash that results in secondary crashes within a minute from a regular crash that does not result in any secondary crashes. Random-effects, random-parameter and two-level binary logistic regression models are applied to data collected on 8779 crashes on the freeway network of the Guizhou Province, China in 2018, of which 299 are consecutive crashes. According to the AIC values, the two-level logistic model outperforms the other two models. Rear-end primary crashes have a significant random effect varying across road segments on the occurrence of consecutive crashes. Various crash types (rear-end, roll-over and side-swipe), tunnel crash and foggy weather are positively associated with the possibility to cause subsequent consecutive crashes, whereas single-vehicle crash, truck involvement and the time periods with poorer natural lighting are less likely to incur consecutive crashes. Recommendations are provided to minimize the possibility of the occurrence of consecutive crashes on a freeway.

A Motorcyclist-Injury Severity Analysis: A Comparison of Single-, Two-, and Multi-Vehicle Crashes Using Latent Class Ordered Probit Model

Motorcycle crashes increasingly become a high proportion of the overall motorized vehicle fatalities. However, limited research has been conducted to compare the injury severity of single-, two- and multi-vehicle crashes involving a motorcycle. This study aims to investigate the effects of rider characteristics, road conditions, pre-crash situations, and crash features on motorcycle severities with respect to different numbers of vehicles involved. The crash data used was obtained through a comprehensive Motorcycle Crash Causation Study (MCCS) by the Federal Highway Administration. An anatomic injury severity indicator, the New Injury Severity Score (NISS), is utilized to calculate a total score as the sum of squared the abbreviated injury scale scores of each of the rider's three most severe injuries. A hybrid approach integrating Latent Class Clustering (LCC) and Ordered Probit (OP) models was used to uncover the unobserved heterogeneity and to explore the major factors which significantly affect the injury severities resulting from single-, two- and multi-vehicle crashes involving a motorcycle. The results show that the significant differences in severity exist between different numbers of vehicles involved. More importantly, they also indicate dividing motorcycle crashes into homogeneous classes before modelling helps to discover insightful information. Pre-speed of the motorcycle is found to be a main factor associated with serious and critical injuries in most types of crashes. Findings of the study provide specific and insightful countermeasures targeting at the contributing factors of motorcycle crashes.

Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data

Traffic violations and improper driving are behaviors that primarily contribute to traffic crashes. This study aimed to develop effective approaches for predicting at-fault crash driver frequency using only city-level traffic enforcement predictors. A fusion deep learning approach combining a convolution neural network (CNN) and gated recurrent units (GRU) was developed to compare predictive performance with one econometric approach, two machine learning approaches, and another deep learning approach. The performance comparison was conducted for (1) at-fault crash driver frequency prediction tasks and (2) city-level crash risk prediction tasks. The proposed CNN-GRU achieved remarkable prediction accuracy and outperformed other approaches, while the other approaches also exhibited excellent performances. The results suggest that effective prediction approaches and appropriate traffic safety measures can be developed by considering both crash frequency and crash risk prediction tasks. In addition, the accumulated local effects (ALE) plot was utilized to investigate the contribution of each traffic enforcement activity on traffic safety in a scenario of multicollinearity among predictors. The ALE plot illustrated a complex nonlinear relationship between traffic enforcement predictors and the response variable. These findings can facilitate the development of traffic safety measures and serve as a good foundation for further investigations and utilization of traffic violation data.

An exploratory analysis of factors associated with traffic crashes severity in Cartagena, Colombia

Traffic fatalities are the second cause of violent deaths in Colombia. However, due to the signing of the peace agreement and the growing number of fatalities in road crashes, it is possible that soon traffic fatalities will be the primary cause of violent deaths in the country, particularly in urban areas. This study is an exploratory analysis focused on identifying the main factors associated with the severity of traffic crashes in urban areas, using Cartagena as a case study. We analyzed three levels of crash severity, namely fatal, injury, and property-damage-only, considering factors in several different dimensions: victim, vehicle, road infrastructure, traffic and control, day and time, and environmental factors. A modeling approach based on multinomial ordered discrete models was used to properly identify the main factors associated with the severity levels. We found that the probability of fatal accidents is higher on streets with speed limits over 40 km/h, and that males and people aged 60 years or older are the victims with the most significant risk of fatal crashes. Motorcycles were also identified as vehicles with the highest probability of fatal crashes in the city. We showed that the probability of fatal crashes occurring is higher on streets where pedestrian bridges, traffic lights, and crosswalks are present. These findings are worthy because, in Colombia and other developing countries, the authorities normally expect to reduce the probability of fatal accidents through investments in pedestrian bridges, signaling devices, and crosswalk markings. However, according to our results, it possibly will not occur unless further countermeasures are taken. Based on these findings, reducing speed limits, operational improvements at signalized intersections, zero tolerance for traffic violations related to pedestrians, an awareness campaign on pedestrian safety focused on males and people aged 60 or older, and improving motorcycle safety are the countermeasures we proposed. Furthermore, as the authorities make significant efforts to investing in pedestrian bridges, we propose a further investigation into the traffic crashes in streets where there is this infrastructure since more severe events occur near them.

Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways

Mountainous highways suffer from high crash rates and fatality rates in many countries, and single-vehicle crashes are overrepresented along mountainous highways. Route familiarity has been found greatly associated with driver behaviour and traffic safety. This study aimed to investigate and compare the contributory factors that significantly influence the injury severities of the familiar drivers and unfamiliar drivers involved in mountainous highway single-vehicle crashes. Based on 3037 cases of mountainous highway single-vehicle crashes from 2015 to 2017, the characteristics related to crash, environment, vehicle and driver are included. Random-effects generalized ordered probit (REGOP) models were applied to model injury severities of familiar drivers and unfamiliar drivers that are involved in the single-vehicle crashes on the mountainous highways, given that the single-vehicle crashes had occurred. The results of REGOP models showed that 8 of the studied factors are found to be significantly associated with the injury severities of the familiar drivers, and 10 of the studied factors are found to significantly influence the injury severities of unfamiliar drivers. These research results suggest that there is a large difference of significant factors contributing to the injury severities between familiar drivers and unfamiliar drivers. The results shed light on both the similar and different causes of high injury severities for familiar and unfamiliar drivers involved in mountainous highway single-vehicle crashes. These research results can help develop effective countermeasures and proper policies for familiar drivers and unfamiliar drivers targetedly on the mountainous highways and alleviate injury severities of mountainous highway single-vehicle crashes to some extent. Based on the results of this study, some potential countermeasures can be proposed to minimize the risk of single-vehicle crashes on different mountainous highways, including tourism highways with a large number of unfamiliar drivers and other normal mountainous highways with more familiar drivers.

A systematic assessment of the use of opponent variables, data subsetting and hierarchical specification in two-party crash severity analysis

Road crashes impose an important burden on health and the economy. Numerous efforts have been undertaken to understand the factors that affect road collisions in general, and the severity of crashes in particular. In this literature several strategies have been proposed to model interactions between parties in a crash, including the use of variables regarding the other party (or parties) in the collision, data subsetting, and estimating models with hierarchical components. Since no systematic assessment has been conducted of the performance of these strategies, they appear to be used in an ad-hoc fashion in the literature. The objective of this paper is to empirically evaluate ways to model party interactions in the context of crashes involving two parties. To this end, a series of models are estimated using data from Canada's National Collision Database. Three levels of crash severity (no injury/injury/fatality) are analyzed using ordered probit models and covariates for the parties in the crash and the conditions of the crash. The models are assessed using predicted shares and classes of outcomes, and the results highlight the importance of considering opponent effects in crash severity analysis. The study also suggests that hierarchical (i.e., multi-level) specifications and subsetting do not necessarily perform better than a relatively simple single-level model with opponent-related factors. The results of this study provide insights regarding the performance of different modelling strategies, and should be informative to researchers in the field of crash severity.