Analysis of driver injury severity in wrong-way driving crashes on controlled-access highways

Comprehensive analysis of trends, distribution, and odds of wrong-way driving fatal crashes on divided highways in the United States (2004-2020)

INTRODUCTION

This study presents a comprehensive analysis of wrong-way driving (WWD) fatal crashes on divided highways in the United States over a 17-year period, from 2004 to 2020. The study aims to uncover trends, distribution patterns, and factors contributing to these fatal crashes. Data were extracted from the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) database.

METHODS

Descriptive statistical analysis was used to reveal general crash characteristics, while trends were updated through an examination of the annual occurrence of WWD fatal crashes. The study further employed binomial logistic regression to compute odds ratios, identifying significant contributing factors. These factors encompassed temporal variables, crash characteristics, and driver characteristics. The odds ratios shed light on the relationship between WWD fatal crashes and other fatal crashes, allowing for the identification of key elements that drive WWD incidents.

RESULTS

On average, 302 WWD fatal crashes occurred annually, resulting in 6,953 fatalities during the study period. The frequency of WWD fatal crashes remained relatively stable, with a slight increase over time. According to the model, variables include day of week, time of day, month, lighting conditions, weather conditions, roadway profile, collision type, passenger presence, driver age, gender, license status, and driver injury severity were found to significantly impact the occurrence of WWD fatal crashes. One significant finding is that road profiles like sag curves or hillcrests can increase the likelihood of WWD fatal crashes.

PRACTICAL APPLICATION

The findings of this study contribute to an improved understanding of WWD fatal crashes on divided highways, thereby aiding in the development of strategies for prevention and mitigation.

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.

Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study

Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained from the traffic police department, in the city of Luzhou, China. Detailed descriptive analysis of the data showed that wrong-way driving was the most common violation type observed. Inverse Distance Weighted (IDW) interpolation in the ArcMap Geographic Information System (GIS) was used to develop violation hotspots zones to guide on efficient use of limited resources during the treatment of high-risk sites. Lastly, a systematic Machine Learning (ML) framework, such as K Nearest Neighbors (KNN) models (using k = 3, 5, 7, 10, and 12), support vector machine (SVM), and CN2 Rule Inducer, was utilized for classification and prediction of each violation type as a function of several explanatory variables. The predictive performance of proposed ML models was examined using different evaluation metrics, such as Area Under the Curve (AUC), F-score, precision, recall, specificity, and run time. The results also showed that the KNN model with k = 7 using manhattan evaluation had an accuracy of 99% and outperformed the SVM and CN2 Rule Inducer. The outcome of this study could provide the practitioners and decision-makers with essential insights for appropriate engineering and traffic control measures to improve the safety of road-users.

Local walking and cycling by residents living near urban motorways: cross-sectional analysis

Background

Everyday activities, such as walking or cycling, may be a feasible and practical way to integrate physical activity into everyday life. Walking and cycling for transport or recreation in the area local to a person’s home may have additional benefits. However, urban planning tends to prioritise car use over active modes. We explored the cross-sectional association between living near an urban motorway and local walking and cycling.

Methods

In 2013, residents living in an area (a) near a new urban motorway (M74), (b) near a longstanding urban motorway (M8), or (c) without a motorway, in Glasgow, Scotland, were invited to complete postal surveys assessing local walking and cycling journeys and socio-demographic characteristics. Using adjusted regression models, we assessed the association between motorway proximity and self-reported local walking and cycling, as well as the count of types of destination accessed. We stratified our analyses according to study area.

Results

One thousand three hundred forty-three residents (57% female; mean age: 54 years; SD: 16 years) returned questionnaires. There was no overall association between living near an urban motorway and the likelihood of local walking or cycling, or the number of types of local destination accessed by foot or bicycle.

In stratified analyses, for those living in the area around the new M74 motorway, increasing residential proximity to the motorway was associated with lower likelihood of local recreational walking and cycling (OR 0.63, 95% CI: 0.39 to 1.00) a pattern not found in the area with the longstanding M8 motorway. In the area near the M8 motorway residential proximity was statistically significantly (p = 0.014) associated with a 12% decrease in the number of types of destination accessed, a pattern not found in the M74 study area.

Conclusions

Our findings suggest that associations between living near a motorway and local walking and cycling behaviour may vary by the characteristics of the motorway, and by whether the behaviour is for travel or recreation. The lack of associations seen in the study area with no motorway suggests a threshold effect whereby beyond a certain distance from a motorway, additional distance makes no difference.

Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming

INTRODUCTION

The state of Wyoming, like other western United States, is characterized by mountainous terrain. Such terrain is well noted for its severe downgrades and difficult geometry. Given the specific challenges of driving in such difficult terrain, crashes with severe injuries are bound to occur. The literature is replete with research about factors that influence crash injury severity under different conditions. Differences in geometric characteristics of downgrades and mechanics of vehicle operations on such sections mean different factors may be at play in impacting crash severity in contrast to straight, level roadway sections. However, the impact of downgrades on injury severity has not been fully explored in the literature. This study is thus an attempt to fill this research gap. In this paper, an investigation was carried out to determine the influencing factors of crash injury severities of downgrade crashes.

METHOD

Due to the ordered nature of the response variable, the ordered logit model was chosen to investigate the influencing factors of crash injury severities of downgrade crashes. The model was calibrated separately for single and multiple-vehicle crashes to ensure the different factors influencing both types of crashes were captured.

RESULTS

The parameter estimates were as expected and mostly had signs consistent with engineering intuition. The results of the ordered model for single-vehicle crashes indicated that alcohol, gender, road condition, vehicle type, point of impact, vehicle maneuver, safety equipment use, driver action, and annual average daily traffic (AADT) per lane all impacted the injury severity of downgrade crashes. Safety equipment use, lighting conditions, posted speed limit, and lane width were also found to be significant factors influencing multiple-vehicle downgrade crashes. Injury severity probability plots were included as part of the study to provide a pictorial representation of how some of the variables change in response to each level of crash injury severity.

CONCLUSION

Overall, this study provides insights into contributory factors of downgrade crashes. The literature review indicated that there are substantial differences between single- and multiple vehicle crashes. This was confirmed by the analysis which showed that mostly, separate factors impacted the crash injury severity of the two crash types. Practical applications: The results of this study could be used by policy makers, in other locations, to reduce downgrade crashes in mountainous areas.

Wrong-way driving crashes: A random-parameters ordered probit analysis of injury severity

In the context of traffic safety, whenever a motorized road user moves against the proper flow of vehicle movement on physically divided highways or access ramps, this is referred to as wrong-way driving (WWD). WWD is notorious for its severity rather than frequency. Based on data from the U.S. National Highway Traffic Safety Administration, an average of 355 deaths occur in the U.S. each year due to WWD. This total translates to 1.34 fatalities per fatal WWD crashes, whereas the same rate for other crash types is 1.10. Given these sobering statistics, WWD crashes, and specifically their severity, must be meticulously analyzed using the appropriate tools to develop sound and effective countermeasures. The objectives of this study were to use a random-parameters ordered probit model to determine the features that best describe WWD crashes and to evaluate the severity of injuries in WWD crashes. This approach takes into account unobserved effects that may be associated with roadway, environmental, vehicle, crash, and driver characteristics. To that end and given the rareness of WWD events, 15 years of crash data from the states of Alabama and Illinois were obtained and compiled. Based on this data, a series of contributing factors including responsible driver characteristics, temporal variables, vehicle characteristics, and crash variables are determined, and their impacts on the severity of injuries are explored. An elasticity analysis was also performed to accurately quantify the effect of significant variables on injury severity outcomes. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions significantly contribute to the injury severity of WWD crashes.

Wrong-way driving crashes: A multiple correspondence approach to identify contributing factors

OBJECTIVE

Wrong-way driving (WWD) crashes result in 1.34 fatalities per fatal crash, whereas for other non-WWD fatal crashes this number drops to 1.10. As such, further in-depth investigation of WWD crashes is necessary. The objective of this study is 2-fold: to identify the characteristics that best describe WWD crashes and to verify the factors associated with WWD occurrence.

METHODS

We collected and analyzed 15 years of crash data from the states of Illinois and Alabama. The final data set includes 398 WWD crashes. The rarity of WWD events and the consequently small sample size of the crash database significantly influence the application of conventional log-linear models in analyzing the data, because they use maximum-likelihood estimation. To overcome this issue, in this study, we employ multiple correspondence analysis (MCA) to define the structure of the crash data set and identify the significant contributing factors to WWD crashes on freeways.

RESULTS

The results of the present study specify various factors that characterize and influence the probability of WWD crashes and can thus lead to the development of several safety countermeasures and recommendations. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions were among the most significant contributors to WWD crashes.

CONCLUSIONS

Despite many other methods that identify only the contributing factors, this method can identify possible associations between various contributing factors. This is an inherent advantage of the MCA method, which can provide a major opportunity for state departments of transportation (DOTs) to select safety countermeasures that are associated with multiple safety benefits.

Using fixed-parameter and random-parameter ordered regression models to identify significant factors that affect the severity of drivers' injuries in vehicle-train collisions

This study attempts to identify significant factors that affect the severity of drivers' injuries when colliding with trains at railroad-grade crossings by analyzing the individual-specific heterogeneity related to those factors over a period of 15 years. Both fixed-parameter and random-parameter ordered regression models were used to analyze records of all vehicle-train collisions that occurred in the United States from January 1, 2001 to December 31, 2015. For fixed-parameter ordered models, both probit and negative log-log link functions were used. The latter function accounts for the fact that lower injury severity levels are more probable than higher ones. Separate models were developed for heavy and light-duty vehicles. Higher train and vehicle speeds, female, and young drivers (below the age of 21 years) were found to be consistently associated with higher severity of drivers' injuries for both heavy and light-duty vehicles. Furthermore, favorable weather, light-duty trucks (including pickup trucks, panel trucks, mini-vans, vans, and sports-utility vehicles), and senior drivers (above the age of 65 years) were found be consistently associated with higher severity of drivers' injuries for light-duty vehicles only. All other factors (e.g. air temperature, the type of warning devices, darkness conditions, and highway pavement type) were found to be temporally unstable, which may explain the conflicting findings of previous studies related to those factors.