Injury-severity analysis of crashes involving defective vehicles and accounting for the underlying socioeconomic mediators.

Crashes occur from a combination of factors related to the driver, roadway, and vehicle factors. The impact of vehicles on road crashes is a critical consideration within road safety analysis, even though not much studies have been conducted in this area. This study assessed how various vehicle and other crash factors are significantly associated with crash outcomes. To do this, historical vehicle defect-related crashes were obtained for the state of Alabama from 2016 to 2020. After data cleaning, a crash injury severity model was developed using the random parameters multinomial logit with heterogeneity in means approach to account for possible unobserved heterogeneity in the data. A spatial analysis was further conducted to better understand vehicle defect crashes as a broader societal issue and potentially explore their connection with the socio-demographic characteristics of the drivers of these vehicles. The preliminary data analysis showed that brake and tire defects accounted for about 65% of the vehicle defects associated with the crashes. The model estimation results revealed that improper tread depth and headlight defects were associated with major injury outcomes, while brake defects were more associated with minor injuries. Also, crashes associated with speeding, drunk driving, failure to use seatbelts, and those that occurred on curved roads left with downgrades were likely to result in major injuries. Findings from the spatial analysis showed that postal codes with higher median incomes are more likely to record lower vehicle defect-related crashes, unlike those that have higher proportions of females and African Americans. The study's findings provide data-driven evidence for sustained safety campaigns, workshops, and training on basic vehicle maintenance practices in the low-income communities in the state.

Stability of factors influencing walking-along-the-road pedestrian injury severity outcomes under different lighting conditions: A random parameters logit approach with heterogeneity in means and out-of-sample predictions.

Pedestrians walking along the road's edge are more exposed and vulnerable than those on designated crosswalks. Often, they remain oblivious to the imminent perils of potential collisions with vehicles, making crashes involving these pedestrians relatively unique compared to others. While previous research has recognized that the surrounding lighting conditions influence traffic crashes, the effect of different lighting conditions on walking-along-the-road pedestrian injury severity outcomes remains unexplored. This study examines the variations in the impact of risk factors on walking-along-the-road pedestrian-involved crash injury severity across various lighting conditions. Preliminary stability tests on the walking-along-the-road pedestrian-involved crash data obtained from Ghana revealed that the effect of most risk factors on injury severity outcomes is likely to differ under each lighting condition, warranting the estimation of separate models for each lighting condition. Thus, the data were grouped based on the lighting conditions, and different models were estimated employing the random parameter logit model with heterogeneity in the means approach to capture different levels of unobserved heterogeneity in the crash data. From the results, heavy vehicles, shoulder presence, and aged drivers were found to cause fatal pedestrian walking-along-the-road severity outcomes during daylight conditions, indicators for male pedestrians and speeding were identified to have stronger associations with fatalities on roads with no light at night, and crashes occurring on Tuesdays and Wednesdays were likely to be severe on lit roads at night. From the marginal effect estimates, although some explanatory variables showed consistent effects across various lighting conditions in pedestrian walking-along-the-road crashes, such as pedestrians aged < 25 years and between 25 and 44 years exhibited significant variations in their impact across different lighting conditions, supporting the finding that the effect of risk factors are unstable. Further, the out-of-sample simulations underscored the shifts in factor effects between different lighting conditions, highlighting that enhancing visibility could play a pivotal role in significantly reducing fatalities associated with pedestrians walking along the road. Targeted engineering, education, and enforcement countermeasures are proposed from the interesting insights drawn to improve pedestrian safety locally and internationally.

An in-depth analysis of head-on crash severity and fatalities in Ghana.

Head-on collisions are often linked to more serious injuries compared to other types of crashes, due to the intense impact they cause. In low- and middle-income countries, these collisions frequently involve high occupancy public transportation vehicles, leading to higher fatality rates per crash. Given the high risk of injury and potential for multiple casualties, this study delves into the factors influencing the outcomes of head-on crashes and the number of fatalities in Ghana. The study analyzed six years of historical head-on collision data from Ghana and developed two models to address the issue. The injury-severity analysis was performed using a random parameter multinomial logit with heterogeneity in means and variances approach and aimed to identify the factors that have a significant impact on the severity of injuries sustained in head-on collisions, while the random parameters negative binomial fatality count model was designed to examine the factors that contribute to the number of fatalities in these crashes in the country. Results showed that head-on collisions with drivers over 65, buses, motorcycles, and those between 25 and 65 years of age were more likely to result in fatalities. Speeding and vehicle malfunctions were also found to be significant contributing factors to fatal head-on collisions. Head-on crashes involving minibuses and incidents where the driver was attempting to overtake another vehicle were found to be more likely to result in a higher number of fatalities. The results of this study uncover an intriguing interaction between human-related elements and socioeconomic factors, which pose obstacles to the Government's endeavor to upgrade the major highways in the country. Additionally, the increasing need for transportation has led to the presence of vehicles on the roads that may not meet safety standards. Consequently, it is no surprise that several of the study's findings align with expectations. Nevertheless, within the specific context of Ghana, these findings furnish compelling data-driven evidence supporting the adoption and implementation of the safe systems approach as a means to tackle fatal head-on collisions in the country.

A comprehensive study of child pedestrian crash outcomes in Ghana.

Pedestrians are considered as one of the vulnerable road user groups. Among pedestrians of all ages, children are the most at risk. Previous studies have shown that children have inadequate knowledge of road safety and are unable to identify risks on road. Despite these limitations associated with children, society places the burden on them to protect themselves. However, to be able to adequately address child pedestrian safety issues there is the need to understand the factors that influence their crash involvement and severity of injury they sustain. To address this gap, this study performed a comprehensive analysis of historical crash data in Ghana to find holistic countermeasures for these crashes. The study used five years of child pedestrian (below 10 years) crash records obtained from the Building and Road Research Institute (BRRI) in Ghana. A temporal analysis of the data revealed that the highest number of the crashes coincide with when school-going children go to and close from school. A random parameter multinomial logit model was developed to identify crash variables that are significantly associated with child pedestrian crash outcomes. The estimation results revealed that children are likely to be killed in crashes when the driver is speeding and inattentive. Also, it was found that children walking along the road, crossing the road, and those in urban areas are more susceptible to incapacitating injury crashes. Male drivers accounted for 95.8% of child pedestrian crashes, and crashes involving male drivers are 7.8% more likely to be fatal. The findings from this study provide a deeper, data-driven understanding of child pedestrian crashes and how temporal characteristics, vehicle type, location of pedestrian, traffic operation, and environmental and human factors affect crash outcomes. These findings will help in developing countermeasures like providing conspicuous pedestrian crossings, footbridges on multi-lane high-speed roadways, and the use of school buses to convey students to help mitigate the number and severity of child pedestrian crashes in Ghana, and by extension other countries in the sub-region.

Association between driving frequency and well-being among older adults.

OBJECTIVES: Research on driving in older adulthood suggests that driving is a form of independence for older adults and is often associated with increased social capital and overall-being. However, few studies have examined whether the frequency of driving, and not driving alone, affects likelihood of having well-being among older adults. This study aimed to examine the association between frequency of driving and well-being among older adults, guided by the activity theory of aging. METHODS: Data were drawn from the 2018 National Health and Aging Trends Study, a longitudinal panel survey of Medicare beneficiaries living in the United States. Bivariate analyses were conducted using Chi-square tests and the association between frequency of driving and well-being was tested with a multivariable logistic regression model. Well-being was determined by 11 items measuring positive and negative affect and asking participants if they agreed with certain statements about their lives. RESULTS: After controlling for other factors that could influence well-being among older adults, results showed that those who drove every day were the most likely to have high well-being, followed by those who drove most days, those who drove some days, those who drove rarely, and those who never drove. DISCUSSION: The study results indicate that as frequency of driving increases, the chance of having well-being increases among older adults. This supports the activity theory of aging and highlights the importance of productive aging.

Severity analysis of single-vehicle left and right run-off-road crashes using a random parameter ordered logit model.

OBJECTIVES: Single vehicle (SV) run-off-road crashes are a major cause of severe injury and fatality. Such crashes can result in different levels of severity depending on the direction (i.e., left or right) in which the vehicle runs off the road. This paper investigated the factors contributing to the crash severities of right run-off-road (R-ROR) and left run-off-road (L-ROR) SV crashes. METHODS: The study used SV crash data from the City of Charlotte, North Carolina, covering 2014 to 2017. Two separate random parameter ordered logit (RPOL) models were developed to estimate the contributing factors to R-ROR and L-ROR SV crash severities. The impact of the explanatory variables on the crash severity outcomes was quantified using the models' direct pseudo-elasticities. RESULTS: The model results showed that male drivers, Driving Under Influence (DUI), motorcycles, and dry road surfaces were significant contributing factors to R-ROR and L-ROR SV crash severities. Specifically for the R-ROR model, speeding, reckless driving, 1-2 lanes, and older drivers increased crash severity. For the L-ROR model, phone distraction, crossed centerline/median, 3-4 lanes, rain, and dark unlighted roadway increased crash severity. CONCLUSIONS: Based on the estimated parameters for the common significant variables in the two models, it was inferred that L-ROR SV crashes are more likely to result in severe crashes compared to R-ROR SV crashes. Hence, this study contributes to the literature on ROR SV crashes by providing additional insight into contextual factors influencing ROR crash severity for more effective countermeasures.

Learning about injury severity from no-injury crashes: A random parameters with heterogeneity in means and variances approach.

The traditional approach to injury-severity analyses does not allow in-depth understanding of no-injury crashes, as crash factors found to contribute to the various injury severities may have similar effects on the severity of vehicle damage even if no injury is recorded. Viewing no-injury crashes using the vehicle damage severities as sub-categories and bases for potential injuries can improve understanding of future injury crashes. To better understand the mechanism of no-injury crashes and the crash factors that contribute to the extent of vehicle damage beyond the single categorization of these crashes in injury severity analysis, this study presents a vehicle damage severity analysis for no-injury crashes. To compare the effects of crash contributing factors on crash outcomes, two injury severity models were also estimated. Random parameters multinomial logit models with heterogeneity in means and variances were developed to account for unobserved heterogeneity. Model estimation results revealed that several common factors (e.g., unsafe speed, distracted driving, driving under influence, vehicle age, and run-off-road) are correlated with both injury severity in injury crashes and vehicle damage severity in no-injury crashes. Therefore, the sub-categorization of no-injury crashes by vehicle damage severity can potentially improve estimates of injury severity considered in resource allocation decisions for traffic safety.

Exploration of motorcyclists' red-light running and helmet use at signalized intersections in Ghana.

OBJECTIVE: There is a dearth of empirical studies on motorcyclists' red-light running and helmet use at signalized intersections in low and middle-income countries like Ghana, Nigeria and Malaysia. This study seeks to fill the gap by looking at red-light running and helmet use at signalized intersections in the Cape Coast metropolis, Ghana. The study also identified potential areas of intervention to reduce the dangers posed by motorcyclists' red-light running in the Cape Coast Metropolis without the use of a helmet. METHOD: A naturalistic exploratory un-obstructive observational approach was used in assessing this phenomenon. The relationship between motorcyclists' behaviors and motorcyclists' observed demographic characteristics, the locality of the intersection, time of the week and presence of pillion passengers were analyzed. Chi-Square test of independence was used to establish the statistically significant relationships between dependent and independent variables. RESULTS: In all, 2,225 motorcyclists and 744 pillion passengers were observed. The results revealed that 33.1% of the motorcyclists ran a red light with 45.4% not using a helmet. Red-light running at signalized intersections was significantly linked to the locality of the intersection, time of the week, and helmet use. The helmet use was low and significantly associated with the presence of a pillion passenger and whether the pillion passenger used a helmet or not. CONCLUSION: Red-light running is influenced by locality of intersection, time of the week and helmet use. Efforts to reduce red-light running and improve helmet use should involve road safety education, awareness creation, and enforcement of traffic laws by the officials of the National Road Safety Authority and Motor Transport and Traffic Department of the Ghana Police Service. City managers in other low and middle-income countries can use the findings in the study to inform policy.

Severity analysis of crashes involving in-state and out-of-state large truck drivers in Alabama: A random parameter multinomial logit model with heterogeneity in means and variances.

The trucking sector contributes significantly to the economic vitality of the United States. Large trucks are primarily used for transporting goods within and across states. Despite its economic importance, large truck crashes constitute public safety concerns. To minimize the consequences, there is a need to understand the factors that contribute to the severity outcomes of truck-involved crashes. Since many large truck drivers transport goods across several states, the driver-centered crash factors are expected to differ between in-state and out-of-state drivers. For this reason, this study developed two random parameters multinomial logit models with heterogeneity in means and variances to examine the factors contributing to the severity of crashes involving in-state and out-of-state large truck drivers in Alabama. The study was based on the 2016-2020 large truck crashes in Alabama. After data cleaning and preparation, it was observed that approximately 20% of in-state and 23% of out-of-state large truck crashes were fatigue-related. There were more speeding related crashes (12.4%) among in-state large truck drivers, but the contribution of speeding to crash severity outcomes was only significant in the out-of-state model. More crashes related to red light running violation (14.2%) were observed among out-of-state drivers, pointing to the fundamental issues of fatigue and unfamiliarity with the operations of signalized intersections in Alabama. The study contributes to the literature on large truck crashes by uncovering the nuances in crashes involving in-state and out-of-state large truck drivers. Despite the seeming similarity in factors that influence crash outcomes, this study provides the basis for truck drivers' training and communication campaigns on the differences that may exist in roadway characteristics from state to state. Also, policy formulations and strategies that prioritizes the well-being of the large truck drivers and creates a better working condition for them should be explored.

How did the COVID-19 pandemic affect road crashes and crash outcomes in Alabama?

With the rising number of cases and deaths from the COVID-19 pandemic, nations and local governments, including many across the U.S., imposed travel restrictions on their citizens. This travel restriction order led to a significant reduction in traffic volumes and a generally lower exposure to crashes. However, recent preliminary statistics in the US suggest an increase in fatal crashes over the period of lockdown in comparison to the same period in previous years. This study sought to investigate how the pandemic affected road crashes and crash outcomes in Alabama. Daily vehicle miles traveled and crashes were obtained and explored. To understand the factors associated with crash outcomes, four crash-severity models were developed: (1) Single-vehicle (SV) crashes prior to lockdown order (Normal times SV); (2) multi-vehicle (MV) crashes prior to lockdown order (Normal times MV); (3) Single-vehicle crashes after lockdown order (COVID times SV); and (4) Multi-vehicle crashes after lockdown order (COVID times MV). The models were developed using the first 28 weeks of crashes recorded in 2020. The findings of the study reveal that although traffic volumes and vehicle miles traveled had significantly dropped during the lockdown, there was an increase in the total number of crashes and major injury crashes compared to the period prior to the lockdown order, with speeding, DUI, and weekends accounting for a significant proportion of these crashes. These observations provide useful lessons for road safety improvements during extreme events that may require statewide lockdown, as has been done with the COVID-19 pandemic. Traffic management around shopping areas and other areas that may experience increased traffic volumes provide opportunities for road safety stakeholders to reduce the occurrence of crashes in the weeks leading to an announcement of any future statewide or local lockdowns. Additionally, increased law enforcement efforts can help to reduce risky driving activities as traffic volumes decrease.

Injury-severity analysis of intercity bus crashes in Ghana: A random parameters multinomial logit with heterogeneity in means and variances approach.

Travel by bus is an efficient, cost-effective, safe and preferred means of intercity transport in many advanced countries. On the contrary, there is huge public sentiment about the safety records of intercity buses in low- and middle-income countries given the increasing bus-involved road traffic crashes and high fatality rates. This study sought to model the injury severity of intercity bus transport in Ghana using the random parameters multinomial logit with heterogeneity in means and variances modelling technique to account for unobserved heterogeneity in the dataset. The dataset involves crash data from the 575 km long Accra-Kumasi-Sunyani-Gonokrom highway in Ghana. Four discrete crash outcome categories were considered in this study: fatal injury, hospitalized injury, minor injury, and no injury. The study observed that crashes involving pedestrians, unlicensed drivers, and drivers and passengers aged more than 60 years have a higher probability of sustaining fatal injuries. Also, speeding, wrong overtaking, careless driving and inexperienced drivers were associated with fatal injury outcomes on the highway. The incidence of intercity bus transport crashes involving larger buses and minibuses were also found to more likely result in fatalities. The probability of hospitalized injury increased for crashes that occurred in a village setting. Given these findings, the study proposed improvement of the road infrastructure, enforcing seatbelt availability and use in intercity buses, increased enforcement of the traffic rules and regulations to deter driver recklessness and speeding as well as improving the luminance of the highways. Additionally, apps that have features for customers to rate intercity bus operators, the quality of services provided, and also have the option to report reckless driving activities can be developed to promote safe and inclusive public transport in the country.

Behavioral pathways in bicycle-motor vehicle crashes: From contributing factors, pre-crash actions, to injury severities.

INTRODUCTION: This study performed a path analysis to uncover the behavioral pathways (from contributing factors, pre-crash actions to injury severities) in bicycle-motor vehicle crashes. METHOD: The analysis investigated more than 7,000 bicycle-motor vehicle crashes in North Carolina between 2007 and 2014. Pre-crash actions discussed in this study are actions of cyclists and motorists prior to the event of a crash, including "bicyclist failed to yield," "motorist failed to yield," "bicyclist overtaking motorist," and "motorist overtaking bicyclist." RESULTS: Model results show significant correlates of pre-crash actions and bicyclist injury severity. For example, young bicyclists (18 years old or younger) are 23.5% more likely to fail to yield to motor traffic prior to the event of a crash than elder bicyclists. The "bicyclist failed to yield" action is associated with increased bicyclist injury severity than other actions, as this behavior is associated with an increase of 5.88 percentage points in probability of a bicyclist being at least evidently injured. The path analysis can highlight contributing factors related to risky pre-crash actions that lead to severe injuries. For example, bicyclists traveling on regular vehicle travel lanes are found to be more likely to involve the "bicyclist failed to yield" action, which resulted in a total 44.38% (7.04% direct effect + 37.34% indirect effect) higher likelihood of evident or severe injuries. The path analysis can also identify factors (e.g., intersection) that are not directly but indirectly correlated with injury severity through pre-crash actions. Practical Applications: This study offers a methodological framework to quantify the behavioral pathways in bicycle-motor vehicle crashes. The findings are useful for cycling safety improvements from the perspective of bicyclist behavior, such as the educational program for cyclists.

Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways.

INTRODUCTION: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. METHOD: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009-2016 crash data from Alabama. RESULTS: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs.

Factors associated with driver injury severity of lane changing crashes involving younger and older drivers.

Lane change maneuvers can be a difficult driving task for many drivers, particularly, younger and inexperienced drivers and older drivers. Frequent and improper lane changing has a substantial effect on both traffic operations and safety. This study investigates the factors that contribute to the injury severity of crashes resulting from improper lane change maneuvers involving younger and older drivers in Alabama. Crash data for the study was obtained from the Critical Analysis Reporting Environment (CARE) system developed by the Center for Advanced Public Safety at the University of Alabama. Preliminary data analysis showed that younger drivers were responsible for about 63.1 % of the crashes. Random logit with heterogeneity in means models were developed to identify significant crash factors that were associated with the crash outcomes. The results show that younger male drivers were more likely to be severely injured in lane changing crashes whereas older male drivers were less likely to be severely injured. The results further show that younger drivers were more likely to sustain a major injury on six-lane highways whereas older drivers sustained major injuries from overtaking maneuvers on two-lane highways. It was further found that while younger drivers got into major injury crashes under daylight conditions, older drivers were more likely to be involved in major injury crashes under dark/unlit lighting conditions. Improper lane changing constitutes some form of aggressive driving which needs to be addressed considering that many lives are lost annually from the practice. The findings from this study may be used by road safety advocates and practitioners to identify what and how to target crash countermeasures to the younger and older driver population groups in the state.

An analysis of risk factors associated with road crash severities in Namibia.

Road crash is a leading cause of death and disabilities in Namibia and other developing countries. Based on recent trends, the World Health Organization indicated that progress to realize Sustainable Development Goal (SDG) target 3.6 - which calls for a 50% reduction in the number of road traffic deaths by 2020 - remains far from sufficient. To contribute to efforts in reducing road fatalities in Namibia, this study examined risk factors associated with the severity of crashes recorded in the country. Mixed logit modelling methodology was adopted to address the problem of unobserved heterogeneity in injury severity analysis. Model estimation results reveal that collision with pedestrians, head-on collisions, ran-off road collisions and crashes involving high occupancy passenger vehicles were more likely to result in fatalities and severe injuries. The findings and recommendations of this study are expected to enhance countermeasure implementation to reduce road crashes in Namibia.

Latent class analysis of factors that influence weekday and weekend single-vehicle crash severities.

This paper investigates factors that influence the severity of single-vehicle crashes that happen on weekdays and weekends. Crash data from 2012 to 2016 for the State of Alabama was used for this study. Latent class logit models were developed as alternative to the frequently used random parameters models to account for unobserved heterogeneity across crash-severity observations. Exploration of the data revealed that a high proportion of severe injury injury crashes happened on weekends. The study examined whether single-vehicle crash contributing factors differ between weekdays and weekends. The model estimation results indicate a significant association of severe injury crashes to risk factors such as driver unemployment, driving with invalid license, no seatbelt use, fatigue, driving under influence, old age, and driving on county roads for both weekdays and weekends. Research findings show a strong link between human factors and the occurrence of severe injury single-vehicle crashes, as it has been observed that many of the factors associated with severe-injury outcome are driver behavior related. To illustrate the significance of the findings of this study, a third model using the combined data was developed to explore the merit of using sub-populations of the data for improved and detailed segmentation of the crash-severity factors. It has also been shown that generally, the factors that influence single-vehicle crash injury outcomes were not very different between weekdays and weekends. The findings of this study show the importance of investigating sub-populations of data to reveal complex relationships that should be understood as a necessary step in targeted countermeasure application.

Multilevel analysis of the role of human factors in regional disparities in crash outcomes.

A growing body of research has examined the disparities in road traffic safety among population groups and geographic regions. These studies reveal disparities in crash outcomes between people and regions with different socioeconomic characteristics. A critical aspect of the road traffic crash epidemic that has received limited attention is the influence of local characteristics on human elements that increase the risk of getting into a crash. This paper applies multilevel logistic regression modeling techniques to investigate the influence of driver residential factors on driver behaviors in an attempt to explain the area-based differences in the severity of road crashes across the State of Alabama. Specifically, the paper reports the effects of characteristics attributable to drivers and the geographic regions they reside on the likelihood of a crash resulting in serious injuries. Model estimation revealed that driver residence (postal code or region) accounted for about 7.3% of the variability in the probability of a driver getting into a serious injury crash, regardless of driver characteristics. The results also reveal disparities in serious injury crash rate as well as significant proportions of serious injury crashes involving no seatbelt usage, driving under influence (DUI), unemployed drivers, young drivers, distracted driving, and African American drivers among some regions. The average credit scores, average commute times, and populations of driver postal codes are shown to be significant predictors for risk of severe injury crashes. This approach to traffic crash analysis presented can serve as the foundation for evidence-based policies and also guide the implementation of targeted countermeasures.