A note on accounting for underlying injury-severity states in statistical modeling of injury accident data

Alternative approaches to modeling heterogeneity to analyze injury severity sustained by motorcyclists in two-vehicle crashes

The presence of unobserved factors in the motorcycle involved two-vehicle crashes (MV) data could lead to heterogenous associations between observed factors and injury severity sustained by motorcyclists. Capturing such heterogeneities necessitates distinct methodological approaches, of which random and scale heterogeneity models are paramount. Herein, we undertake an empirical evaluation of random and scale heterogeneity models, exploring their efficacy in delineating the influence of external determinants on the degree of injury severity in crashes. Within the effects of scale heterogeneity, this study delves into two dominant models: the scaled multinomial logit model (S-MNL) and its generalized counterpart, the G-MNL, which encompasses both the S-MNL and the random parameters multinomial logit model (RPL). While the random heterogeneity domain is represented by the random parameters multinomial logit and an upgraded variant - the random parameters multinomial logit model with heterogeneity in means and variances (RPLHMV). Motorcycle involved two-vehicle crashes data were extracted from the UK STATS19 dataset from 2016 to 2020. Likelihood ratio tests are computed to assess the temporal variability of the significant factors. The test result demonstrates the temporal variations over a five-year study period. Some very important differences started to show up across the years based on the model estimation results: that the RPLHMV model statistically outperforms the G-MNL model in the 2016, 2018, and 2019 models, while the S-MNL model is statistically superior in the 2017 and 2020 years. These important findings suggest that the origin of heterogeneity in explaining factor weights can be captured by scale effects, not just random heterogeneity. In addition, the model results further show that motorcyclists' injury severities are significantly affected by motorcycle-related characteristics; there is the added factor of external influences, such as non-motorcycle drivers (males, young drivers, and elderly drivers) and vehicles (the moving status, age, and types of vehicles) that collide with motorcycles. The results of this paper are anticipated to help policymakers develop effective strategies to mitigate motorcycle involved two-vehicle crashes by implementing appropriate measures.

Bivariate joint analysis of injury severity of drivers in truck-car crashes accommodating multilayer unobserved heterogeneity

Truck-involved crashes, especially truck-car crashes, are associated with serious and even fatal injuries, thus necessitating an in-depth analysis. Prior research focused solely on examining the injury severity of truck drivers or developed separate performance models for truck and car drivers. However, the severity of injuries to both drivers in the same truck-car crash may be interrelated, and influencing factors of injury severities sustained by the two parties may differ. To address these concerns, a random parameter bivariate probit model with heterogeneity in means (RPBPHM) is applied to examine factors affecting the injury severity of both drivers in the same truck-car crash and how these factors change over the years. Using truck-car crash data from 2017 to 2019 in the UK, the dependent variable is defined as slight injury and serious injury or fatality. Factors such as driver, vehicle, road, and environmental characteristics are statistically analyzed in this study. According to the findings, the RPBPHM model demonstrated a remarkable statistical fit, and a positive correlation was observed between the two drivers' injury severity in truck-car crashes. More importantly, the effects of the explanatory factors showing relatively temporal stability vary across different types of vehicle crashes. For example, car driver improper actions and lane changing by trucks, have a significant interactive effect on the severity of injuries sustained by drivers involved collisions between trucks and cars. Male truck drivers, young truck drivers, older truck drivers, and truck drivers' improper actions, elevate the estimated odds of only truck drivers; while older car and unsignalized crossing increase the possibility of injury severity of only car drivers. Finally, due to shared unobserved crash-specific factors, the 30-mph speed limit, dark no lights, and head-on collision, significantly affect the severity of injuries sustained by drivers involved in collisions between trucks and cars. The modeling approach provides a novel framework for jointly analyzing truck-involved crash injury severities. The findings will help policymakers take the necessary actions to reduce truck-car crashes by implementing appropriate and accurate safety countermeasures.

Assessing School Travel Safety in Scotland: An Empirical Analysis of Injury Severities for Accidents in the School Commute

School travel has been a significant source of safety concerns for children, parents, and public authorities. It will continue to be a source of concerns as long as severe accidents continue to emerge during pupils’ commute to school. This study provides an empirical analysis of the factors influencing the injury severities of the accidents that occurred on trips to or from school in Scotland. Using 9-year data from the STATS19 public database, random parameter binary logit models with allowances for heterogeneity in the means were estimated in order to investigate injury severities in urban and rural areas. The results suggested that factors such as the road type, lighting conditions, vehicle type, and age of the driver or casualty constitute the common determinants of injury severities in both urban and rural areas. Single carriageways and vehicles running on heavy oil engines were found to induce opposite effects in urban and rural areas, whereas the involvement of a passenger car in the accident decomposed various layers of unobserved heterogeneity for both area types. The findings of this study can inform future policy interventions with a focus on traffic calming in the proximity of schools.

An analysis of single-vehicle truck crashes on rural curved segments accounting for unobserved heterogeneity

INTRODUCTION

Medium to large truck crashes, particularly on rural curved roadways, lead to a disproportionately higher number of fatalities and serious injuries relative to other passenger vehicles over time. The intent of this study is to identify and quantify the factors affecting injury severity outcomes for single-vehicle truck crashes on rural curved segments in North Carolina. The crash data were extracted from the Highway Safety Information System (HSIS) from 2010 to 2017.

METHOD

This study applied a mixed logit with heterogeneity in means and variances approach to model driver injury severity. The approach accounts for possible unobserved heterogeneity in the data resulting from driver, roadway, vehicle, traffic characteristics and/or environmental conditions. Results' Conclusion: The model results indicate that there is a complex interaction of driver characteristics such as demographics (male and female drivers, age below 30 years, and age between 50 to 65 years), driver physical condition (normal driving condition and sleepy while driving), driver actions (unsafe speed, overcorrection, and careless driving), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (undivided road, medium right shoulder width, graded surface, low and medium speed limit, low traffic volume), environmental conditions (rainy condition), vehicle characteristics (tractor-trailer and semi-trailer), and crashes characteristics (fixed object crashes and rollover crashes). In addition, this study compared the contributing factor leading to driver injury severity for curved and straight rural segments. Practical Applications: The results clearly indicate the importance of driving behavior, such as, exceeding the speed limit and careless driving along the high-speed curved segments, need to be prioritized for the trucking agency. Similarly, the suggested countermeasures for roadway design and maintenance agency encompass warning signs and advisory speed limit, roadside barrier with chevrons, and edge line rumble strips are important concerning curved segments in rural highways.

Do High Visibility Enforcement programs affect aggressive driving behavior? An empirical analysis using Naturalistic Driving Study data

This paper investigates the effect of High Visibility Enforcement (HVE) programs on different types of aggressive driving behavior, namely, speeding, tailgating, unsafe lane changes and 'other' aggressive driving behavior types (occurrence of not-yielding right-of-way and red light or stop signs violations). For this purpose, the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data are used, which include forward-facing videos and time series information with regard to trips conducted at or near the locations of HVE implementation. To capture the intensity and duration of speeding and tailgating, scaled metrics are developed. These metrics can capture varying levels of aggressive driving behavior enabling, thus, a direct comparison of the various behavioral aspects over time and among different drivers. To identify the effect of HVE and other trip, driver, vehicle or environmental factors on speeding and tailgating, while accounting for possible interrelationship among the behavior-specific scaled metrics, Seeming Unrelated Regression Equation (SURE) models were developed. To analyze the likelihood of occurrence of unsafe lane changes and 'other' aggressive driving behavior types, a grouped random parameters ordered probit model with heterogeneity in means and a correlated grouped random parameters binary logit model were estimated, respectively. The results showed that drivers' awareness of HVE implementation has the potential to decrease aggressive driving behavior patterns, especially unsafe lane changes and 'other' aggressive driving behaviors.