The effect of motorcyclists' age on injury severities in single-motorcycle crashes with unobserved heterogeneity

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000-10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Supervised and unsupervised techniques to analyze risk factors associated with motorcycle crash

PURPOSE

Motorcycles are one of the highly used modes of transport in Barcelona, Spain, in particular, and in many different regions, in general. This situation is compromising safety on the road and may be attributed to a potential increase in traffic crashes. Therefore, this study examines several risk factors and their consequential impacts on the level of injury that is resulted in case of a traffic crash.

METHODS

Two approaches are employed to analyze the risk factors, including a supervised learning technique which is a binary probit model, and an unsupervised technique which is the Kohonen clustering.

RESULTS

The results for both models show that alcoholism and road in poor condition can indeed increase the probability of having different levels of injuries as reasons for the crash. Elderly users are less likely to be involved in motorcycle crash injuries compared to other age categories, especially the age group that ranges from 25 to 40 years old which has the highest odds. For both techniques, the performance in analyzing the utilized data shows that both approaches can be successfully utilized for this type of dataset.

CONCLUSION

This study highlights the considerable danger faced by motorcyclists due to various risk factors. It stresses the critical importance of maintaining roads in optimal condition not just for efficient travel but also to enhance motorcyclists' safety. Additionally, the research underscores the significant threat posed by speeding, particularly exceeding speed limits, to motorcyclists' safety, emphasizing the urgent need for more 30 km/h speed limit zones and stricter enforcement of speed regulations. As a result, the research has identified several risk factors that increase the likelihood of severe or fatal injuries among motorcyclists in Barcelona and has suggested certain recommendations to mitigate their impact.

Examining factors affecting driver injury severity in speeding-related crashes: a comparative study across driver age groups

This paper investigates the factors influencing the severity of driver injuries in single-vehicle speeding-related crashes, by comparing different driver age groups. This study employed a random threshold random parameter hierarchical ordered probit model and analysed crash data from Thailand between 2012 and 2017. The findings showed that young drivers face a heightened fatality risk when speeding in passenger cars or pickup trucks, hinting at the role of inexperience and risk-taking behaviours. Old drivers exhibit an increased fatality risk when speeding, especially in rainy conditions, on flush median roads, and during evening peak hours, attributed to reduced reaction times and vulnerability to adverse weather. Both young and elderly drivers face escalated fatality risks when speeding on road segments lacking guardrails during adverse weather, with older drivers being particularly vulnerable in rainy conditions. All age groups show an elevated fatality risk when speeding on barrier median roads, underscoring the significant role of speeding, which increases crash impact and limits margins of error and manoeuvrability, thereby highlighting the need for safety measures focusing on driver behaviour. These findings underscore the critical imperative for interventions addressing not only driver conduct but also road infrastructure, collectively striving to curtail the severity of speeding-related crashes.

Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve 'unintended pedestrians', drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not 'typical pedestrians', a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014-2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian's FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35-44 years, and spring season. The interaction of 'pedestrian impairment' and 'weekend' was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the 'unintended pedestrians' about the crash scenarios on the interstate and reduce these unexpected incidents.

Evaluating helmet-wearing of single-vehicle overspeeding motorcycle crashes: Insights from temporal instability in parsimonious pooled framework

OBJECTIVE

A lower helmet-wearing rate and overspeeding in Pakistan are critical risk behaviors of motorcyclists, causing severe injuries. To explore the differences in the determinants affecting the injury severities among helmeted and non-helmeted motorcyclists in motorcycle crashes caused by overspeeding behavior, single-vehicle motorcycle crash data in Rawalpindi city for 2017-2019 is collected. Considering three possible crash injury severity outcomes of motorcyclists: fatal injury, severe injury and minor injury, the rider, roadway, environmental, and temporal characteristics are estimated.

METHODS

To provide a mathematically simpler framework, the current study introduces parsimonious pooled random parameters logit models. Then, the standard pooled random parameters logit models without considering temporal effects are also simulated for comparison. By comparing the goodness of fit measure and estimation results, the parsimonious pooled random parameters logit model is suitable for capturing the temporal instability. Then, the non-transferability among helmeted and non-helmeted overspeeding motorcycle crashes is illustrated by likelihood ratio tests and out-of-sample prediction, and two types of models provide robust results. The marginal effects are also calculated.

RESULTS

Several variables, such as age, cloudy and weekday indicators illustrate temporal instability. Moreover, several variables are observed to only show significance in non-helmeted models, showing non-transferability across helmeted and non-helmeted models.

CONCLUSIONS

More educational campaigns, regulation and enforcement, and management countermeasures should be organized for non-helmeted motorcyclists and overspeeding behavior. Such findings also provide research reference for the risk-compensating behavior and self-selected group issues under overspeeding riding considering the usage of helmets.

Assessing the effects of geometry and non-geometry related factors in work-zone crashes

OBJECTIVE

Work zones are unique in geometry and traffic management, utilizing special traffic signs, standard channelizing devices, appropriate barriers, and pavement markings. These configurations can introduce unexpected driving conditions, potentially posing risks to drivers. This analysis aims to explore potential differences in contributing factors between work-zone crashes where geometry was identified as a factor and those where it was non-geometry factor. To gain insights into driver injury severities in single-vehicle work-zone crashes, this study analyzed work zone crash data from Florida.

METHOD

This study employed random parameters logit models, accommodating potential variations in parameter estimates' means and variances. The dataset encompassed a wide array of factors known to influence driver injury severity, encompassing crash characteristics, vehicle attributes, roadway features, prevailing traffic volume, driver profiles, and spatial and temporal considerations.

RESULTS

This analysis yielded significantly distinct parameters for work-zone crashes, distinguishing between geometry-related and non-geometry-related factors (primarily the human factors). This distinction suggests a complex interplay between these factors. Notably, the marginal effects of individual parameter estimates exhibited marked differences between these two categories - geometry and non-geometry factors.

CONCLUSION

These findings contribute to the growing body of research indicating that geometric restrictions within work zones introduce a distinct set of risk factors compared to non-geometry-related factors. Recognizing the significance of geometric restrictions, beyond typical driving conditions, holds the implications for enhancing safety within various work zone configurations and offers valuable insights for crash scene investigators to pinpoint contributing factors accurately.

Unraveling the differences in distracted driving injury severities in passenger car, sport utility vehicle, pickup truck, and minivan crashes

Distracted driving poses a significant risk on the roadway users, with the level of distraction and crash outcomes varying depending on the type of vehicle. Drivers of passenger cars, sport utility vehicles (SUVs), pickup trucks, minivans experience distinct levels of distraction, leading to potential crashes. This study investigates into the severity of driver injuries resulting from distracted driving in these vehicle categories, shedding light on the variations in single-vehicle crashes. Focusing on single-vehicle crashes in Florida during 2019, involving passenger cars, SUVs, pickup trucks, and minivans caused by distracted driving, the study examines various distractions such as, electronic communication devices (cell phones), electronic devices (navigation systems, music players), internal and external disturbances, texting, and inattentive driving. To analyze the severity of injuries resulting from distracted driving in passenger cars, SUVs, pickup trucks, and minivans, the study employs random parameter multinomial logit models with heterogeneity in means and variances. The model estimates highlight thirty-five significant factors influencing the severity of driver injuries resulting from distracted driving. Notably, the impact of these factors varies significantly depending on the vehicle type (i.e., passenger cars, SUVs, pickup trucks, and minivans). While many explanatory variables are specific to each vehicle type, only one factor (restraint belt usage) is common across all vehicle types, with varying magnitudes in injury outcomes. The likelihood ratio tests indicate that injury severity must be analyzed and modeled separately for passenger cars, SUVs, pickup trucks, and minivans. Vehicle characteristics play a crucial role in driver distraction and crash outcomes. Analyzing a year of crash data, categorized by four vehicle types, has provided valuable insights into distracted driving patterns in passenger cars, SUVs, pickup trucks, and minivans, influencing potential prevention strategies. To combat against distracted driving effectively, priority should be given to driver education and training, roadway design, vehicle technology, enforcement, and automobile insurance. The automobile industry, especially for passenger cars, SUVs, pickup trucks, and minivans, should consider implementing advanced in-vehicle technologies tailored to the specific characteristics of each vehicle type (e.g., advanced driver assistance systems (ADAS)) to proactively prevent driver distraction. These proactive measures will contribute significantly to enhancing road safety and reducing the risks associated with distracted driving.

Injury severity analysis of single-vehicle and two-vehicle crashes with electric scooters: A random parameters approach with heterogeneity in means and variances

In recent years, the electric scooter has become one of the most popular means of transportation on short trips. Due to the lag in the formulation of transportation policies and regulations, coupled with the increasing number of electric scooter crashes, there has been growing concern about the safety of pedestrians and electric scooter riders. For the first time in the extant literature, this study aims to analyze injury severity of electric scooter crashes by unobserved heterogeneity modeling approaches. A random parameters approach with heterogeneity in means and variances is utilized to examine the factors influencing injury severity, using data collected from the STATS19 road safety database. Electric scooter crashes are classified as single-vehicle crashes and two-vehicle crashes, with injury severity categorized into two groups: fatalities or serious injuries, and slight injuries. The model estimation was conducted by considering several variables including roadway, environment, temporality, vehicle, and rider characteristics, as well as second-party vehicle and driver characteristics and manners of collision specific to two-vehicle crashes. The results of the model estimation reveal that certain factors had relatively stable effects with the varying degree of crash injury severity outcomes in both single-vehicle crashes and two-vehicle crashes. These factors include nighttime incidents, weekdays, male riders, and an increase in rider age, all of which are associated with more severe injury outcomes. Moreover, the random parameters logit model with heterogeneity in means and variances is more flexible in accounting for unobserved heterogeneity and exhibits better goodness of fit. This study improves the understanding of electric scooter safety, and the finding can better inform public policy regarding electric scooter use to improve road safety and reduce injury severity of electric scooter crashes.

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.

Nationwide Analysis of Motorcycle-Associated Injuries and Fatalities in the United States: Insufficient Prevention Policies or Abandoned Laws?

BACKGROUND

Motorcycle road traffic collisions are a major cause of mortality in the United States. We aimed to analyze the temporal and statewide trends in motorcycle collision fatalities (MCFs) nationwide and their association with state laws regarding motorcycle helmet requirements, lane splitting, speeding, intoxicating driving, and red light cameras.

METHODS

A retrospective review of United States MCF/capita from 2015 to 2019 was performed using the Fatality Analysis Reporting System database. MCF/capita was defined as a motorcyclist death per 100 000 motorcyclist registrations. Independent-samples t-test and ANOVA were used to determine differences, with significance defined as P < .05. Linear regression analysis and Pearson's correlation were used to further determine associations between variables.

RESULTS

The majority of fatalities occurred in males (n = 21 354, 91.0%), ages 25-54 (n = 13 728, 58.5%), and Caucasians (n = 19 195, 81.8%). A total of 24 states and DC exhibited positive trends in MCF/capita from 2015 to 2019. There was no significant difference in MCF/capita between states who had mandatory helmet laws for all, partial requirements, and states with no law (63.4 vs 54.3 vs 33.6, P = .360). Among fatalities involving alcohol, a significantly greater number of MCF/capita were found above the legal limit of .08 compared to the group with a blood alcohol concentration of .01-.07 (17.8 vs 4.5, P < .001).

CONCLUSION

Motorcyclist fatalities continue to pose a public health risk, with 24 states showing an upward trend. Additional interventions and laws are needed to decrease the number of motorcyclist deaths. Further strategy on implementation and enforcement of helmet laws and alcohol consumption may be an essential component.

Differences in single-vehicle motorcycle crashes caused by distraction and overspeed behaviors: considering temporal shifts and unobserved heterogeneity in prediction

Distraction and overspeed behaviors are acknowledged as two significant contributors to single-vehicle motorcycle crashes, injuries and fatalities resulting from which are severe and critical issues in Pakistan. To explore the temporal instability and differences in the factors determining the injury severities between single-vehicle motorcycle crashes caused by distraction and overspeed behaviors, this study estimated two groups of random parameter logit models with heterogeneity in means and variances. Single-vehicle motorcycle crash data in Rawalpindi city between 2017 and 2019 was used for model estimation, and a wide variety of explanatory variables relating to the rider, roadways, environments, and temporal attributes was simulated in the models. The current study considered three possible crash injury severity outcomes: minor injury, severe injury and fatal injury. Likelihood ratio tests were conducted to explore the temporal instability and non-transferability. Marginal effects were also calculated to further reveal temporal instability of the variables. Except for several variables, the most significant factors reported temporal instability and non-transferability, manifested as the effects varied from year to year and across different crashes. Moreover, out-of-sample prediction was also implemented to capture temporal instability and non-transferability between distraction and overspeed crash observations. The non-transferability between motorcycle crashes caused by distraction and overspeed behaviors provides insights into developing differentiated countermeasures and policies targeted at preventing and mitigating single-vehicle motorcycle crashes caused by the two risk-taking behaviors.

Motorcyclist injury severity analysis: a comparison of Artificial Neural Networks and random parameter model with heterogeneity in means and variances

In Thailand, the motorcyclist mortality rate is steadily on the rise and remains a serious concern for highway administrators and burden on both economic and local people. Using motorcycle-crash data in Thailand from 2016 to 2019, this study empirically employed and compared the Artificial Neural Networks (ANN) model and random parameters binary probit model with heterogeneity in means and variances (RPBPHM) to explore the effects of a wide range of associated risk characteristics on the severity outcomes of the motorcyclist. Study results revealed that probabilities of injury or fatal crash increase for crashes that involve male riders, riding with pillion, speeding, improper overtaking, riders under influence of alcohol, fatigue riders, undivided road and so on. The probability of non-injury crash increases for crashes on main or frontage traffic lane, four-lane road, concrete road, during rain, involving collision with other motorcycles, rear-end crashes, sideswipe crashes, single-motorcycle crashes and crashes within urban areas. The RPBPHM models were found to outperform the ANN model (quadratic support vector machine) in all performance metrics. The findings could potentially assist policymaker, safety professionals, practitioners, trainers, government agencies or highway designers in future planning and serve as guidance for mitigation policies directed at safety improvement for motorcyclists.

An empirical analysis of driver injury severities in work-zone and non-work-zone crashes involving single-vehicle large trucks

OBJECTIVES

Florida ranks among the states with the highest rates of work-zone crashes involving large trucks. With significant emphasis in Florida's strategic highway safety plan, understanding work-zone crashes involving large trucks and resulting injury severities is critically important. This study investigated the contributing factors influencing the driver injury severity of single-large-truck crashes in work zones, benchmarked against non-work zones in Florida.

METHODS

Using work-zone and non-work-zone crash data from 2011 to 2019 (inclusive), driver-injury severities in single-large trucks crashes were studied using random parameters logit models that allow for possible heterogeneity in the means and variances of parameter estimates. The available data included a wide variety of factors known to influence driver injury severity, including spatial and temporal; vehicle and traffic; roadway, harmful events, and driver characteristics.

RESULTS

The model estimates produced fundamental shift in unobserved heterogeneity for work-zone and non-work-zone crashes involving single large trucks. More importantly, the likelihood of large truck drivers' injury severity is about fourteen-times higher on rural and six-times higher on urban interstate highways and 1.3 times lower with 10 miles per hour below the posted speed limit for large trucks inside work zones relative to non-work zones. The model results also indicate that the likelihood of severe driver injury is higher for heavy truck (more than 26000 pounds), a lane-shift work-zone configuration, and careless driving in work-zone crashes involving single large trucks.

CONCLUSIONS

The model findings add valuable insights to have profound effects in the safety performance of large trucks and in-vehicle safety technologies, such as, Advanced Driver Assistance Systems, for careful driving along the work-zone segments with lower speed, leading to Automated Driving Systems. These measures include various policy-related safety countermeasures including revisiting traffic control plan for lane-shift on highways specifically for large trucks and developing training modules for Florida registered truck drivers.

Modeling the temporal relationship between contributing factors and injury severities in rural single-vehicle alcohol-impaired driving crashes: Insights from random parameters logit models in the means and variances

OBJECTIVES

Alcohol-impaired driving (A-ID) crashes have been acknowledged as fatality-concentrated while there is a limited understanding of how contributors relating to A-ID influence crash severity and lead to more severe injuries in rural areas. The current paper utilized North Carolina crash data to investigate the unobserved heterogeneity and temporal stability of the rural single-vehicle A-ID crash injury-severity determinants over a five-year period from 2014-2018.

METHODS

Crash injury severities were estimated using a group of random parameters logit models in the means and variances with three categories of injury-severity determined as outcome variables including no injury, minor injury, and severe injury. Explanatory variables were selected across multiple factors that could be classified as roadway characteristics, environmental characteristics, crash characteristics, temporal characteristics, vehicle characteristics and driver characteristics. The temporal stability of the models was examined through a series of likelihood ratio tests. Marginal effects were also adopted to analyze the temporal stability of the explanatory variables.

RESULTS

The result uncovers an overall temporal instability. Some contributors present relatively temporal stability such as female, turning, passenger car, motorcycle, vehicle age (5-9 years old), speed limit (<45 mph), curved segment, dry road surface, animal collision and overturned collision. Curved segment and dry road surface are found to consistently increase the possibility of severe injuries in rural alcohol-involved crashes.

CONCLUSIONS

This paper can provide insights into preventing single-vehicle A-ID crashes and could potentially facilitate the development of single-vehicle A-ID crash injury mitigation policies in rural areas. More studies could be conducted adopting the advanced data-driven methods for A-ID crash prediction.

What Factors Would Make Single-Vehicle Motorcycle Crashes Fatal? Empirical Evidence from Pakistan

The existing research on motorcycle safety has shown that single-vehicle motorcycle crashes (SVMC) account for a higher fatality rate than other types of crashes. Also, motorcycle safety has become one of the critical traffic safety issues in many developing countries, such as Pakistan, due to the growing number of motorcycles and lack of sufficient relevant infrastructure. However, the available literature on SVMC and motorcycle safety in developing countries is limited. Therefore, the present study attempted to investigate the factors that contribute to the injury severity of SVMC in a developing country, Pakistan. For this purpose, a random parameter logit model with heterogeneity in means and variances is developed using two years of data extracted from the road traffic injury research project in Karachi city, Pakistan. The study's findings show that the presence of pillion passengers and young motorcyclists indicators result in random parameters with heterogeneity in their means and variances. The study's results also reveal that the summer, morning time, weekends, older motorcyclists, collisions with fixed objects, speeding, and overtaking are positively, while younger motorcyclists and the presence of pillion passengers are negatively associated with fatal crashes. More importantly, in the particular Pakistan's context, female pillion passenger clothes trapped in the wheel, riding under the influence, intersections, U-turns, and collisions due to loss of control are also found to significantly influence the injury severity of SVMC. Based on these research findings, multiple appropriate countermeasures are recommended to enhance motorcycle safety in Pakistan and other developing countries with similar problems.

Bicyclists injury severities: An empirical assessment of temporal stability

Cyclists are among the most vulnerable participants in road traffic, making their safety a top priority. Riding behavior of bicyclists could shift over time, affecting the level of injuries sustained in bicyclist-involved crashes. Many studies have been done to identify the factors influencing bicyclist injury severity, but the temporal stability of these variables over time needs further study. The temporal instability of components that affect the cyclist injury levels in bicycle collisions is explored in this paper. To obtain potential unobserved heterogeneity, yearly models of cyclist-injury levels (including potential consequences of no, minor, and severe injury) were measured separately applying a random parameters logit model that allows for potential heterogeneity in estimated parameters' means and variances. Employing a data source on bicycle collisions in Los Angeles, California, over the course of six years (January 1, 2012 to December 31, 2017), several variables which may impact the injury level of cyclists were explored. This paper has also employed a set of likelihood ratio tests assessing the temporal instability of the models. The temporal instability of the explanatory parameters has been evaluated with marginal effects. The results of the model assessment indicate that several factors may raise the chances of severe bicyclist injuries in collisions, including cyclists older than 55 years old, cyclists who were identified to be at-fault in crashes, rear-end collisions, cyclists who crossed into opposing lane before the collision, crashes occurring early mornings (i.e., 00:00 to 06:00) and so on. The results also showed that the details and estimated parameters of the model do not remain stable over the years, however the source of this instability is unclear. In addition, the findings of model estimation demonstrate that considering the heterogeneity in the random parameter means and variances will enhance the overall model fit. This study also emphasizes the significance of accounting for the transferability of estimated models and the temporal instability of parameters influencing the injury severity outcomes in order to dynamically examine the collected data and adjust safety regulations according to new observations.

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.