The odds of wrong-way crashes and resulting fatalities: A comprehensive analysis

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.

Identification of high-risk roadway segments for wrong-way driving crash using rare event modeling and data augmentation techniques

Wrong-Way Driving (WWD) crashes are relatively rare but more likely to produce fatalities and severe injuries than other crashes. WWD crash segment prediction task is challenging due to its rare nature, and very few roadway segments experience WWD events. WWD crashes involve complex interactions among roadway geometry, vehicle, environment, and drivers, and the effect of these complex interactions is not always observable and measurable. This study applied two advanced Machine Learning (ML) models to overcome the imbalanced dataset problem and identified local and global factors contributing to WWD crash segments. Five years (2015-2019) of WWD crash data from Florida state were used in this study for WWD model development. The first modeling approach applied four different hybrid data augmentation techniques to the training dataset before applying the XGBoost classification algorithm. In the second model, a rare event modeling approach using the Autoencoder-based anomaly detection method was applied to the original data to identify WWD roadway segments. A third model was applied based on the statistical method to compare the performance of ML models in predicting the WWD segments. The performance comparison of the adopted models showed that the XGBoost model with the Adaptive Synthetic Sampling (ADASYN) method performed best in terms of precision and recall values compared to the autoencoder-based anomaly detection method. The best-performing model was used for the feature analysis with an interpretable machine-learning technique. The SHapley Additive exPlanations (SHAP) values showed that high-intensity developed land use, length of roadway, log of Annual Average Daily traffic (AADT), and lane width were positively associated with WWD roadway segments. The results of this study can be used to deploy WWD countermeasures effectively.

Factors affecting bus accident severity in Thailand: A multinomial logit model

Bus accidents are a serious issue, with high rates of injury and fatality in Thailand. However, no studies have been conducted on the factors affecting bus accident severity in Thailand. A cross-sectional study was conducted by the Department of Highways, Thailand over the 2010–2019 period. A multinomial logit model was used to evaluate the factors associated with bus accident severity. This model divided accidents into three categories: non-injury, injury, and fatality. The risk factors consisted of three major categories: the bus driver, characteristics of the crash, and environmental characteristics. The results showed that characteristics of the bus driver, the crash, and the environment where the crash occurred all increased the probability of bus accidents causing injury. These three main factors included driving on sloped roads (relative risk ratio [RRR] 3.03, 95% confidence level [CI] 1.73 to 5.30), drowsy driving (RRR 2.60, 95% CI 1.71 to 3.96), and driving in the wrong direction (RRR 2.37, 95% CI 1.77 to 3.19). Moreover, the factors that increased the probability of the accidents causing fatality were drowsy driving (RRR 3.40, 95% CI 2.07 to 5.57) and drivers not obeying or following traffic rules (RRR 3.02, 95% CI 1.95 to 4.67), especially in the northern part of Thailand (RRR 3.01, 95% CI 1.98 to 4.62). The results can provide a valuable resource to help road authorities in development targeting road safety programs at sloped roads in the northern part of Thailand. Stakeholders should increase road safety efforts and implement campaigns, such as raising public awareness of the risks of not obeying or following traffic rules and drowsy driving which could possibly reduce the risk of both injury and fatality.

Modeling the risk of wrong-way driving at the exit ramp terminals of partial cloverleaf interchanges

INTRODUCTION

Partial cloverleaf (parclo) interchanges with closely spaced parallel entrance and exit ramps are more prone to wrong-way driving (WWD) compared to other interchange types. In this study, a logistic regression model was developed to predict the risk of WWD at the exit ramp terminals of parclo interchanges.

METHOD

The logistic regression model was developed using Firth's penalized likelihood techniques based on the predictor variables such as exit ramp geometric design features, wrong-way related traffic control devices, area type, and traffic volume.

RESULTS

According to the model, the significant predictors of WWD at parclo exit ramp terminals include corner radius from crossroad to entrance ramp, type of median on crossroad, width of median on two-way ramp, channelizing island, distance to the nearest access point, "Keep Right" sign, wrong-way arrow, intersection signalization, and traffic volume at the exit and entrance ramps. This model was used to conduct network screening for all the exit ramp terminals of parclo interchanges in Alabama and Georgia to identify high-risk locations in these two states. Seven high-risk locations were monitored by video cameras for 48-hours to observe the occurrences of WWD incidents. Results suggest that two locations in Alabama and two locations in Georgia experienced multiple WWD incidents within 48-hours of a typical weekend.

CONCLUSION

The observation of WWD incidents at high-risk locations demonstrates strong evidence that the model could identify the exit ramp terminals with high risk of WWD.

PRACTICAL APPLICATIONS

Transportation agencies can use this model to assess the risk of WWD at the exit ramp terminals within their jurisdictions and identify the high-risk locations for countermeasures implementation.

Modeling Wrong-way Driving (WWD) crash severity on arterials in Florida

Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel. WWD studies and mitigation strategies have exclusively been focused on limited-access facilities. However, it has been established that WWD crashes on arterial corridors are also severe and relatively more common. As such, this study focused on determining factors influencing the severity of WWD crashes on arterials. The analysis was based on five years of WWD crashes (2012-2016) that occurred on state-maintained arterial corridors in Florida. Police reports of 2,879 crashes flagged as "wrong-way" were downloaded and individually reviewed. The manual review of the police reports revealed that of the 2,879 flagged WWD crashes, only 1,890 (i.e., 65.6 %) occurred as a result of a vehicle traveling the wrong way. The Bayesian partial proportional odds (PPO) model was used to establish the relationship between the severity of these WWD crashes and different driver attributes, temporal factors, and roadway characteristics. The following variables were significant at the 90 % Bayesian Credible Interval (BCI): day of the week, lighting condition, presence of work zone, crash location, age and gender of the wrong-way driver, airbag deployment, alcohol use, posted speed limit, speed ratio (i.e., driver's speed over the posted speed limit), and the manner of collision. Based on the model results, specific countermeasures on Education, Engineering, Enforcement, and Emergency response are discussed. Potential Transportation Systems Management and Operations (TSM&O) strategies for WWD detection systems on arterials to minimize WWD frequency and severity are also proposed.

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.

Bayesian spatial-temporal model for the main and interaction effects of roadway and weather characteristics on freeway crash incidence

This study attempts to examine the main and interaction effects of roadway and weather conditions on crash incidence, using the comprehensive crash, traffic and weather data from the Kaiyang Freeway in China in 2014. The dependent variable is monthly crash count on a roadway segment (with homogeneous horizontal and vertical profiles). A Bayesian spatio-temporal model is proposed to measure the association between crash frequency and possible risk factors including traffic composition, presence of curve and slope, weather conditions, and their interactions. The proposed model can also accommodate the unstructured random effect, and spatio-temporal correlation and interactions. Results of parameter estimation indicate that the interactions between wind speed and slope, between precipitation and curve, and between visibility and slope are significantly correlated to the increase in the freeway crash risk, while the interaction between precipitation and slope is significantly correlated to the reduction in the freeway crash risk, respectively. These findings are indicative of the design and implementation of real-time traffic management and control measures, e.g. variable message sign, that could mitigate the crash risk under the adverse weather conditions.

Hidden figures behind two-vehicle crashes: An assessment of the risk and external costs of drunk driving in Spain

In this paper, we estimate the relative risk that drunk drivers pose on sober drivers, passengers and pedestrians, and quantify the external cost of drunk driving in Spain between 2004-2015. Eventually we arrive at the following conclusions. Firstly, we find the relative risk of drunk drivers causing a crash during the night to be between 2.7-3.9 times higher than that of sober drivers. Secondly, our results point to a decline in drunk driving offences alongside an increase in its punition, mainly after the implementation of the Penalty Points System for driving licenses in Spain on July 1st 2006. We estimate that drunk drivers should be fined by 1250€, in order to offset its external costs. Overall, our assessment indicates a downturn in the external costs of drunk driving over the last decade in Spain.

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.

Investigation on the wrong way driving crash patterns using multiple correspondence analysis

Wrong way driving (WWD) has been a constant traffic safety problem in certain types of roads. Although these crashes are not large in numbers, the outcomes are usually fatalities or severe injuries. Past studies on WWD crashes used either descriptive statistics or logistic regression to determine the impact of key contributing factors. In conventional statistics, failure to control the impact of all contributing variables on the probability of WWD crashes generates bias due to the rareness of these types of crashes. Distribution free methods, such as multiple correspondence analysis (MCA), overcome this issue, as there is no need of prior assumptions. This study used five years (2010-2014) of WWD crashes in Louisiana to determine the key associations between the contribution factors by using MCA. The findings showed that MCA helps in presenting a proximity map of the variable categories in a low dimensional plane. The outcomes of this study are sixteen significant clusters that include variable categories like determined several key factors like different locality types, roadways at dark with no lighting at night, roadways with no physical separations, roadways with higher posted speed, roadways with inadequate signage and markings, and older drivers. This study contains safety recommendations on targeted countermeasures to avoid different associated scenarios in WWD crashes. The findings will be helpful to the authorities to implement appropriate countermeasures.

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.

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

For more than five decades, wrong-way driving (WWD) has been notorious as a traffic safety issue for controlled-access highways. Numerous studies and efforts have tried to identify factors that contribute to WWD occurrences at these sites in order to delineate between WWD and non-WWD crashes. However, none of the studies investigate the effect of various confounding variables on the injury severity being sustained by the at-fault drivers in a WWD crash. This study tries to fill this gap in the existing literature by considering possible variables and taking into account the ordinal nature of injury severity using three different ordered-response models: ordered logit or proportional odds (PO), generalized ordered logit (GOL), and partial proportional odds (PPO) model. The findings of this study reveal that a set of variables, including driver's age, condition (i.e., intoxication), seatbelt use, time of day, airbag deployment, type of setting, surface condition, lighting condition, and type of crash, has a significant effect on the severity of a WWD crash. Additionally, a comparison was made between the three proposed methods. The results corroborate that the PPO model outperforms the other two models in terms of modeling injury severity using our database. Based on the findings, several countermeasures at the engineering, education, and enforcement levels are recommended.