Types Of Traffic Crashes Research Articles

Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

Although hazardous material (HAZMAT) truck-involved crashes are uncommon compared to other types of traffic crashes, these crashes pose considerable threats to the public, property, and environment due to the unique feature of low probability with high consequences. Using ten-year (2010–2019) crash data from the Fatality Analysis Reporting System (FARS) database, this study applies cluster correspondence analysis to identify the underlying patterns and the associations between the risk factors for HAZMAT-truck-involved fatal crashes. A low-dimensional space projects the categorical variables (including the crash, road, driver, vehicle, and environmental characteristics) into different clusters based on the optimal clustering validation criterion. This study reveals that fatal HAZMAT-truck-involved crashes are highly distinguishable concerning collision types (angle and front-to-front crashes, single-vehicle crashes, and front-to-end crashes) and roadway geometric variables, such as two-way undivided roadways, curve alignments, and high-speed (65 mph or more) urban interstate highways. Driver behavior (distraction, asleep or fatigue, and physical impairment), lighting conditions (dark–lighted and dark–not lighted), and adverse weather are also interrelated. The findings from this study will help HAZMAT carriers, transportation management authorities, and policymakers develop potential targeted countermeasures for HAZMAT-truck-involved crash reduction and safety improvement.

Exploring Relationships between Months and Different Crash Types on Mountainous Freeways Using a Combined Modeling Approach

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions.

Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data.

Although crashes involving hazardous materials (HAZMAT) are rare events compared with other types of traffic crashes, they often cause tremendous loss of life and property, as well as severe hazards to the environment and public safety. Using five-year (2013–2017) crash data (N = 1610) from the Highway Safety Information System database, a two-step machine learning-based approach was proposed to investigate and quantify the statistical relationship between three HAZMAT crash severity outcomes (fatal and severe injury, injury, and no injury) and contributing factors, including the driver, road, vehicle, crash, and environmental characteristics. Random forest ranked the importance of risk factors, and then Bayesian networks were developed to provide probabilistic inference. The results show that fatal and severe HAZMAT crashes are closely associated with younger drivers (age less than 25), driver fatigue, violation, distraction, special roadway locations (such as intersections, ramps, and bridges), higher speed limits (over 66 mph), midnight and early morning (12:00–5:59 a.m.), head-on crashes, more than four vehicles, fire/explosion/spill, poor lighting conditions, and adverse weather conditions. The overall prediction accuracy of 85.8% suggests the effectiveness of the proposed method. This study extends machine learning applications in a HAZMAT crash analysis, which would help develop targeted countermeasures and strategies to improve HAZMAT road transportation safety.

A multivariate heterogeneous-dispersion count model for asymmetric interdependent freeway crash types

A multivariate count model is developed by introducing a simple and practical formula. The formulation begins with a modification of the standard ordered response model to adopt the count outcomes nature. This modification is accomplished by introducing a non-linear asymmetric interdependence structure among the error terms using the copula-based model. To avoid simulation maximum-likelihood for evaluating the multi-outcome density, we utilize the composite marginal likelihood (CML) approach. The proposed copula-based model with the CML approach allows for asymmetric (tail) dependency without a need for a simulation mechanism. Non-parametric graphical techniques with the empirical copula as well as conventional goodness-of-fit statistics are utilized to guide copula selection. In addition, unobserved heterogeneity across observations is also addressed through a heterogeneous dispersion parameter in the proposed model. The heterogeneous dispersion parameter model is a suitable alternative to random parameter count models in that captures heterogeneity in variance, while allowing for closed form while the latter needs numerical integration or simulation.We apply these techniques to study the interdependence structure among four types of traffic crashes using three years (2005–2007) of cross-sectional crash data record for 274 multilane freeway segments in the State of Washington, USA. These four categories of crash types are the rear end; sideswipe; fixed objects and other crash types. The empirical results show a significant presence of unobserved heterogeneous dependency across these types of crashes. The results indicate the important role of unobserved heterogeneity in variance and covariance structure estimation. An important outcome of this result is that it can affect inference on the relative impact of roadway geometrics on crash occurrence. For example, we find that horizontal curve related parameters on freeway segments substantially increase the joint likelihood of rear-end, sideswipe, fixed objects and other crash types, when compared to the characteristics of vertical curves.

The Safety of Transnational Imported Second-Hand Cars: A Case Study on Vehicle-to-Vehicle Crashes in Romania

Certain features of imported second-hand cars (e.g., age, degree of wear and tear, technical design) can increase their likelihood for traffic crashes. Three official datasets which cover an eight year period (2008–2015) are used to test the connection between importation of second-hand cars and different types of traffic crashes. The traffic crashes database was provided by the Traffic Department of the General Inspectorate of Romanian Police (GIRPTD). The car registration database was provided by Driving-License and Vehicles-Registration Direction (DLVRD). Right-hand driving (RHD) cars database was provided by the Romanian Automotive Registry (RAR). A spatio-temporal visualization of data was performed using Geographic Information System (GIS) while for the statistical analysis we use regression models and Pearson-Correlation-coefficient. The analysis suggests that a significant part of the variation in the volume of traffic accidents can be explained by the volume of imported second-hand cars at the county level. Moreover, an even stronger direct relation exists between the number of imported second-hand cars and Severe Traffic Accidents but also in the case of RHD imported second-hand cars. The overall impact of imported second-hand cars on the traffic safety in Romania is significant but small in comparison to other types of car registration. Study results belong to the category of empirical evidence production which can improve the quality of existing traffic regulations focused both on organizing and ensuring traffic safety, and on the policy of sustainable transport infrastructure development.

Leukoaraiosis, a Common Brain Magnetic Resonance Imaging Finding, as a Predictor of Traffic Crashes

BackgroundThere are no reported studies on the relationship between traffic crashes and brain tissue changes in healthy drivers. The relationship between traffic crashes and leukoaraiosis, a common magnetic resonance imaging finding, was investigated in this study.MethodsA total of 3,930 automobile drivers (2,037 men and 1,893 women; age, 21–87 years) who underwent brain magnetic resonance imaging as part of total health check-ups and answered a road traffic questionnaire were examined to determine whether asymptomatic leukoaraiosis was associated with various types of traffic crashes. Multiple logistic regression analysis was performed to elucidate the relationship between leukoaraiosis and various types of traffic crashes.ResultsSubcortical leukoaraiosis was diagnosed in 28.52% of all subjects, whereas periventricular leukoaraiosis was diagnosed in 9.57% of all subjects. Adjusted odds ratios for involvement in all types of traffic crashes were not significant for subjects with periventricular leukoaraiosis; however, they were significant for subjects with multiple and large multiple subcortical leukoaraiosis. Adjusted odds ratios for involvement in crashes at crossroads were 1.09 (95% confidence interval [CI], 0.60–2.00) for subjects with single subcortical leukoaraiosis, 3.35 (95% CI, 2.36–4.77) for subjects with multiple subcortical leukoaraiosis, and 2.45 (95% CI, 2.36–4.98) for subjects with large multiple subcortical leukoaraiosis. Periventricular leukoaraiosis was not significantly associated with crossroad crashes. Involvement in crashes of any type, parking lot crashes, and rear-end collisions showed no significant association with either subcortical or periventricular leukoaraiosis.ConclusionsMultiple subcortical leukoaraiosis, but not periventricular leukoaraiosis, is significantly associated with traffic crashes, in particular, crossroad crashes. This association is independent of sex, age, and driving exposure. To our knowledge, this is the first evidence describing the relationship between brain tissue changes and traffic crashes.