Investigation of factors affecting the injury severity of single-vehicle rollover crashes: A random-effects generalized ordered probit model

Factors influencing the road accidents in low and middle-income countries: a systematic literature review

This paper studies the main factors affecting road traffic accidents (RTAs) using a systematic review. The primary focus is on factors related to road characteristics and driver behaviours. This review also addresses the socioeconomic and demographic factors to provide a clear overview of which groups suffer the most from RTAs. Several factors were found to affect RTAs, notably road characteristics: highways, high-speed roads, unplanned intersections and two-way roads without dividers; driver behaviours: reckless/aggressive driving and riding, excessive speeding, unawareness of traffic laws, and not using safety equipment; and vehicle types: four and two-wheeled. This review found that male and economically productive people with less education were mostly associated with RTAs. In addition, for most of the low and middle-income countries analyzed, there is a lack of quality data relating to RTAs. Nevertheless, this review provides researchers and policy makers with a better understanding of road accidents for improving road safety.

An investigation of heterogeneous impact, temporal stability, and aggregate shift in factors affecting the driver injury severity in single-vehicle rollover crashes

Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years' models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity.

From sky to road: Incorporating the satellite imagery into analysis of freight truck-related crash factors

Freight truck-related crashes in urban contexts have caused significant economic losses and casualties, making it increasingly essential to understand the spatial patterns of such crashes. Limitations regarding data availability have greatly undermined the generalizability and applicability of certain prior research findings. This study explores the potential of emerging geospatial data to delve deeply into the determinants of these incidents with a more generalizable research design. By synergizing high-resolution satellite imagery with refined GIS map data and geospatial tabular data, a rich tapestry of the road environment and freight truck operations emerges. To navigate the challenges of zero-inflated issues of the crash datasets, the Tweedie Gradient Boosting model is adopted. Results reveal a pronounced spatial heterogeneity between highway and urban non-highway road networks in crash determinants. Factors such as freight truck activity, intricate road network patterns, and vehicular densities rise to prominence, albeit with varying degrees of influence across highways and urban non-highway terrains. Results emphasize the need for context-specific interventions for policymakers, encompassing optimized urban planning, infrastructural overhauls, and refined traffic management protocols. This endeavor may not only elevate the academic discourse around freight truck-related crashes but also champion a data-driven approach towards safer road ecosystems for all.

Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n  =  471) were fatal crashes, while the remaining 40% (n  =  321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

Effectiveness of road safety interventions: An evidence and gap map

Background

Road Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low- and middle-income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high-income countries have successfully reduced RTI by using a public health approach and implementing evidence-based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high-income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge.

Objectives

The objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels.

Search Methods

The EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019.

Selection Criteria

The interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post-crash pre-hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non-fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes.

Data Collection and Analysis

The EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case-control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2.

Main Results

The EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors (n = 771), road design, infrastructure and traffic control (n = 661), legal and institutional framework (n = 424), post-crash pre-hospital care (n = 118) and vehicle factors and protective devices (n = 111). Fatal crashes as outcomes were reported in 1414 records and non-fatal injury crashes in 1252 records. Among the four intermediate outcomes, speed was most commonly reported (n = 298) followed by alcohol (n = 206), use of seatbelts (n = 167), and use of helmets (n = 66). Ninety-six percent of the studies were reported from high-income countries (HIC), 4.5% from upper-middle-income countries, and only 1.4% from lower-middle and low-income countries. There were 25 systematic reviews of high quality, 4 of moderate quality, and 293 of low quality.

Authors' Conclusions

The EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence-synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions.

Applying the heteroskedastic ordered probit model on injury severity for improved age and gender estimation

OBJECTIVE

Driver characteristics have been linked to the frequency and severity of car crashes. Among these, age and gender have been shown to impact both the possibility and severity of a crash. Previous studies have used standard ordered probit (OP) models to analyze crash data, and some research has suggested heteroskedastic ordered probit (HETOP) could provide improved model fit. The objective of this paper is to evaluate potential improvements of the heteroskedastic ordered probit (HETOP) model compared to the standard ordered probit (OP) model in crash analysis, by examining the effect of gender across age on injury severity among drivers. This paper hypothesizes that the HETOP model can provide a better fit to crash data, by allowing heteroskedasticity in the distribution of injury severity across driver age and gender.

METHODS

Data for 20,222 crashes were analyzed for North Carolina from 2016 to 2018, which represents the state with the highest number of fatalities per 100 million vehicle miles traveled amongst available crash data from the Highway Safety Information System.

RESULTS

Darker lighting conditions, severe road surface conditions, and less severe weather were associated with increased injury severity. For driver demographics, the probability of severe injuries increased with age and for male drivers. Moreover, the variance of severity increased with age disproportionately within and across genders, and the HETOP was able to account for this.

CONCLUSIONS

The results of the two applied approaches revealed that HETOP model outperformed the standard OP model when measuring the effects of age and gender together in injury severity analysis, due to the heteroskedasticity in injury severity within gender and age. The HETOP statistical method presented in this paper can be more broadly applied across other contexts and combinations of independent variables for improved model prediction and accuracy of causal variables in traffic safety.

An exploratory investigation into the influence of risk factors on driver injury severity in angle crashes: A random parameter bivariate ordered probit model approach

OBJECTIVE

Angle crashes have been acknowledged as a concerning issue in the traffic safety field, though there is limited understanding of the contributions of risk factors to injury severity. This article aims to examine the impact of risk factors and unobserved heterogeneity on the severity of driver injuries in angle collisions by utilizing angle crash data in the United States from 2016 to 2021.

METHODS

The relationship between risk factors and driver injury severities in angle crashes was investigated using a random parameter bivariate ordered probit model (RPBOP) with 4 categories of injury severity classified as outcome variables, including no injury, possible injury, minor injury, and serious jury. Risk factors were considered as explanatory variables, classified as driver characteristics, vehicle characteristics, road characteristics, environmental characteristics, time characteristics, and crash characteristics. Bayesian inference was used to assess the unobserved heterogeneity in risk factors, and marginal effects were computed to analyze the effect of each factor on injury outcomes.

RESULTS

The findings demonstrate that risk factors have varying effects on driver involvement in angle crashes. Certain factors exhibited unobserved heterogeneity, including young drivers (ages 25-44), older drivers (over age 59), road grade, and collision point orientation. On the other hand, other factors, such as female gender, motorcycles, intersections, speed limit (>50 mph), poor lighting conditions, adverse weather, urban areas, and workdays, were shown to significantly increase the likelihood of driver injury in angle collisions, as well as increase susceptibility to fatal injury.

CONCLUSIONS

This article offers new insights into reducing driver injuries in angle crashes and has the potential to inform policy development aimed at preventing such incidents. Further research could utilize multisource data fusion and investigate the spatiotemporal stability of risk factors to enhance the generalizability of angle collision prevention strategies.

Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes

Upon detecting a crash impact, the vehicle restraint system locks the driver in place. However, external factors such as speeding, crash mechanisms, roadway attributes, vehicle type, and the surrounding environment typically contribute to the driver being jostled within the vehicle. As a result, it is crucial to model unrestrained and restrained drivers separately to reveal the true impact of the restraint system and other factors on driver injury severities. This paper aims to explore the differences in factors affecting injury severity for seatbelt-restrained and unrestrained drivers involved in speeding-related crashes while accounting for temporal instability in the investigation. Utilizing crash data from Thailand between 2012 and 2017, mixed logit models with heterogeneity in means and variances were employed to account for multi-layered unobserved heterogeneity. For restrained drivers, the risk of fatal or severe crashes was positively associated with factors such as male drivers, alcohol influence, flush/barrier median roadways, sloped roadways, vans, running off the roadway without roadside guardrails, and nighttime on unlit or lit roads. For unrestrained drivers, the likelihood of fatal or severe injuries increased in crashes involving older drivers, alcohol influence, raised or depressed median roadways, four-lane roadways, passenger cars, running off the roadway without roadside guardrails, and crashes occurring in rainy conditions. The out-of-sample prediction simulation results are particularly significant, as they show the maximum safety benefits achievable solely by using a vehicle's seatbelt system. Likelihood ratio test and predictive comparison findings highlight the considerable combined impact of temporal instability and the non-transferability of restrained and unrestrained driver injury severities across the periods studied. This finding also demonstrates a potential reduction in severe and fatal injury rates by simply replicating restrained driver conditions. The findings should be of value to policymakers, decision-makers, and highway engineers when developing potential countermeasures to improve driver safety and reduce the frequency of severe and fatal speeding-related single-vehicle crashes.

A temporal analysis of crash injury severities in multivehicle crashes involving distracted and non-distracted driving on tollways

Distracted driving is a prominent cause of traffic crashes and may increase the severity of collisions. Due to the larger speeds on toll ways, distracted driving crashes are more severe than on other types of roads, making it worthwhile to investigate. This study examined the variation in the influence of factors affecting injury severity in crashes involving distracted and non-distracted driving, as well as the change over time, using crash data from Florida toll ways from the 2017 to 2019. A series of random parameters logit models with heterogeneity in the means and variances were developed to analyze different driver-injury severities (no injury, minor injury, and severe injury) in crashes involving distracted and non-distracted driving. In addition, likelihood ratio tests were conducted to determine whether model parameters differed between different driver behaviors (distracted/non-distracted driving) and among years. Several factors potentially impacting injury severities were studied, including driver, crash, vehicle, roadway, environment, temporal, and others. Significant disparities were observed between the contributing factors of the severity of crashes involving distracted and non-distracted driving. Results showed that considerable differences were also observed in the severity of injuries caused by two types of crashes and distracted driving resulted in more serious crashes than non-distracted driving. Despite model results indicated that factors influencing injury severity altered over time, several factors, such as motorcycle involvement and commercial car involvement, still exhibited relative temporal stability in non-distracted driving crashes over the three years. Temporal instability and non-transferability were also captured by out-of-sample predictions to verify the temporal shifts of contributing variables from year to year. This study is useful for distinguishing the influence mechanisms between the two types of crashes involving distracted and non-distracted driving, and the results can be applied for safety countermeasures development.

Exploring factors affecting the injury severity of freeway tunnel crashes: A random parameters approach with heterogeneity in means and variances

Generally, freeway tunnels are built to overcome the complex driving environments in mountainous terrains. However, crashes in these tunnels can be more severe than those on the open road sections due to their closed driving environment. Despite the higher crash severity, very few studies have attempted to investigate the severity of injuries in freeway tunnel crashes. Also, the existing studies on the injury severity analysis of tunnels did not fully consider the unobserved heterogeneity and its interactive effects. To address these issues, the present study first collected a comprehensive dataset containing five-year of police-reported tunnel crashes from Hunan province, China. A random parameters model with heterogeneity in means and variances was then developed to explore the influence of different variables related to the environment, drivers, crashes, vehicles, and tunnels. The study observed that the presence of curves and speeding indicators produce random parameters with heterogeneity in means and variances for freeway tunnels, which is influenced by the young drivers and outside exit zone variables. Also, the results reveal that factors, including weekdays, daytime, speeding, fatigue driving, rear-end collisions, collisions with fixtures, large passenger vehicles, and downgrades increase, while rain reduces the probability of severe injury outcomes in freeway tunnel crashes. More importantly, considering the unique tunnel driving environment, the summer, young drivers, novice drivers, presence of curves, and different tunnel sections (access, entrance, and outside exit zones) also significantly affect the risk of severe injury outcomes. Finally, the study's findings could be used as a basis for developing plans and technologies to minimize the severity of crash injuries in freeway tunnels.

Analysis of driver-injury severity: a comparison between speeding and non-speeding driving crash accounting for temporal and unobserved effects

This paper aims to investigate the differences between temporal stability of factors influencing driver-injury severities in crashes involving speeding and non-speeding driving using a six-year (2012-2017) crash data in Thailand. With two possible driver injury severity outcomes (no/minor and severe/fatal), random parameter binary logit models, that allow for heterogeneity in means and variances, were estimated to fully account for unobserved heterogeneities (i.e., allow crash-level factors to vary across crashes and to influence random parameter distribution). While most factors were unstable over time, speeding crash models result showed that stable factors decreasing probability of severe/fatal injury were restraint, van, passenger car, pickup truck, running-off-road on straight and hitting guardrail and mounting traffic island; whereas stable factor increasing probability of severe/fatal injury were central/eastern/southern regions. In non-speeding driving crash model, stable factors decreasing probability of severe/fatal injury were restraint, truck, and running-off-road on straight and hitting guardrail; whereas stable factors increasing probability of severe and fatal injury were under influence of alcohol and van. The findings of this research could potentially be utilized to improve highway safety and facilitate the development of more effective crash injury mitigation policies. Practical-related recommendation based on the results is also provided.

Investigating the Difference in Factors Contributing to the Likelihood of Motorcyclist Fatalities in Single Motorcycle and Multiple Vehicle Crashes

In order to better understand the factors affecting the likelihood of motorcyclists' fatal injuries, motorcycle-involved crashes were investigated based on the involvement of the following vehicles: single motorcycle (SM), multiple motorcycles (MM) and motorcycle versus vehicle (MV) crashes.

METHOD

Binary logit and mixed logit models that consider the heterogeneity of parameters were applied to identify the critical factors that increase the likelihood of motorcyclist fatality.

RESULTS

Mixed logit models were found to have better fitting performances. Factors that increase the likelihood of motorcyclist fatality include lanes separated by traffic islands, male motorcyclists, and riding with BAC values of less than the legally limited value. Collisions with trees or utility poles lead to the highest likelihood of fatality in SM crashes. The effects of curved roads, same-direction swipe crashes, youth, and unlicensed motorcyclists are only significant in the likelihood of fatality in SM crashes.

CONCLUSIONS

Motorcyclists tend to be killed if they collide with large engine-size motorcycles and vehicles, unlicensed motorcyclists, or drivers with speeding related or right-of-way violations with positive BAC values. Driving or riding should be prohibited for any amount of alcohol or for anyone with a positive BAC value. Law enforcement should focus on unlicensed, speeding motorcyclists and drivers, and those who violate the right of way or perform improper turns. Roadside objects and facilities should be checked for appropriate placement and be equipped with reflective devices or injury protection facilities.

Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data

When traffic collisions occur on urban expressways, the consequences, including injuries, the loss of lives, and damage to properties, are more serious. However, the existing research on the severity of expressway traffic collisions has not been deeply explored. The purpose of this research was to investigate how various factors affect the severity of urban expressway collisions. The severity of urban expressway collisions was set as the dependent variable, which could be divided into three categories: slight collisions, severe collisions, and fatal collisions. Ten variables, including individual characteristics, collision characteristics, and road environment conditions, were selected as independent factors. Based on 975 valid urban expressway collisions, an ordered logistic regression model was established to evaluate the impacts of influence factors on the severity of these crashes. The results show that gender, collision modality, road pavement conditions, road surface conditions, and visibility are significant factors that affect the severity of urban expressway collisions. Females were more likely to be involved in more severe urban expressway collisions than males. For collisions involving pedestrians and non-motorized vehicles, the risk of more severe injury was 7.508 times higher than that associated with vehicle-vehicle collisions. The probability of more severe collisions on urban expressways with poor pavement conditions and wet surface conditions is greater than that on urban expressways with good pavement conditions and dry surface conditions. In addition, as visibility increases, the probability of more severe collisions on urban expressways gradually decreases. These results provide more effective strategies to reduce casualties as a result of urban expressway collisions.

Application of Extremely Randomised Trees for exploring influential factors on variant crash severity data

Crash severity models play a crucial role in evaluating the influencing factors in the severity of traffic crashes. In this study, Extremely Randomised Tree (ERT) is used as a machine learning technique to analyse the severity of crashes. The crash data in the province of Khorasan Razavi, Iran, for a period of 5 years from 2013 to 2017, is used for crash severity model development. The dataset includes traffic-related variables, vehicle specifications, vehicle movement, land use characteristics, temporal characteristics, and environmental variables. In this paper, Feature Importance Analysis (FIA), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots are utilised to analyse and interpret the results. According to the results, the involvement of vulnerable road users such as motorcyclists and pedestrians alongside traffic-related variables are among the most significant variables in crash severity. Results show that the presence of motorcycles can increase the probability of injury crashes by around 30% and almost double the probability of fatal crashes. Analysing the interaction of PDPs shows that driving speeds above 60 km/h in residential areas raises the probability of injury crashes by about 10%. In addition, at speeds higher than 70 km/h, the presence of pedestrians approximately increases the probability of fatal crashes by 6%.

Risk Levels Classification of Near-Crashes in Naturalistic Driving Data

Identifying dangerous events from driving behavior data has become a vital challenge in intelligent transportation systems. In this study, we compared machine and deep learning-based methods for classifying the risk levels of near-crashes. A dataset was built for the study by considering variables related to naturalistic driving, temporal data, participants, and road geometry, among others. Hierarchical clustering was applied to categorize the near-crashes into several risk levels based on high-risk driving variables. The adaptive lasso variable model was adopted to reduce factors and select significant driving risk factors. In addition, several machine and deep learning models were used to compare near-crash classification performance by training the models and examining the model with testing data. The results showed that the deep learning models outperformed the machine learning and statistical models in terms of classification performance. The LSTM model achieved the highest performance in terms of all evaluation metrics compared with the state-of-the-art models (accuracy = 96%, recall = 0.93, precision = 0.88, and F1-measure = 0.91). The LSTM model can improve the classification accuracy and prediction of most near-crash events and reduce false near-crash classification. The finding of this study can benefit transportation safety in predicting and classifying driving risk. It can provide useful suggestions for reducing the incidence of critical events and forward road crashes.

Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest

Accidents involving heavy vehicles are of significant concern as it poses a higher risk of fatality to both heavy vehicle drivers and other road users. This study is carried out based on the heavy vehicle crash data of 2014, extracted from the MIROS Road Accident and Analysis and Database System (M-ROADS). The main objective of this study is to identify significant variables associated with categories of injury severity as well as classify and predict heavy vehicle drivers’ injury severity in Malaysia using the classification and regression tree (CART) and random forest (RF) methods. Both CART and RF found that types of collision, driver errors, number of vehicles involved, driver’s age, lighting condition and types of heavy vehicle are significant factors in predicting the severity of heavy vehicle drivers’ injuries. Both models are comparable, but the RF classifier achieved slightly better accuracy. This study implies that the variables associated with categories of injury severity can be referred by road safety practitioners to plan for the best measures needed in reducing road fatalities, especially among heavy vehicle drivers.

Analysis of Crash Severity of Texas Two Lane Rural Roads Using Solar Altitude Angle Based Lighting Condition

Many studies have examined the impact of factors affecting accident severity in rural areas; however, little attention has been paid to different lighting conditions (LCs), and less to the detailed categories and precise determining of twilight. In this paper, solar altitude angle (SAA), as a basis for differentiating and categorizing LCs, is proposed to investigate explanatory variables in much greater detail. For each LC, namely, dark, twilight, dark lit (dark with street lights) and daylight, separate random parameter models are developed to investigate the impacts of some factors on crash injury severity data of 2017 and 2018 in two lane rural roads of Texas. The model estimation results indicated that different LCs have various contributing factors, indeed, to each injury severity, further stressing the significance of investigating crashes based on SAA. The key differences include crash location, marked lane, grade direction, no passing zone, shoulder width, weekday and collision type. The important findings were that developing artificial lighting at intersections and LED raised pavement markers on two lane rural roads could lead to enhanced road safety under dark LCs. Furthermore, increasing shoulder width in straight segments of two lane rural roads is important for decreasing severe injury in daylight conditions.

Investigating factors influencing rollover crash risk on mountainous interstates

INTRODUCTION

The risk of rollover crashes on mountainous roads is a major concern for transportation authorities due to adverse weather conditions and complex topography. Such crashes incur hazardous consequences on road users' lives. Therefore, it is crucial to identify the contributing factors that give rise to these severe crashes in order to identify preventive measures. Furthermore, exploring the potential sources of heterogeneity of rollover crash contributing factors is equally important.

METHOD

By having a dataset of single-vehicle crashes that occurred on mountainous curved sections in Wyoming, we applied a random parameters, otherwise known as mixed, logit model to identify the factors contributing to the increased risk of rollovers. Vehicle, driver, roadway, environmental, and crash attributes variables were considered as potential predictors in the model. Then, random parameters were identified to uncover the unobserved effects.

RESULTS

Weather, road surface conditions, and speeding were found to have a significant impact on rollover crash risk. These factors were also found to exhibit unobserved heterogeneity effects, which could be attributed to the drivers' responses and conditions. Furthermore, it was found that the propensity of rollovers was higher for sports utility vehicles (SUVs) and pick-up trucks among other vehicle types.

CONCLUSIONS

The results indicated that investigating the impact of these factors on the risk of rollovers while taking into account unobserved heterogeneity effects is an essential step for implementing countermeasures to reduce the frequency and severity of rollover crashes. Practical applications: This study uncovered insights into the factors that lead vehicles to overturn. This aids in suggesting appropriate safety countermeasures that mitigate the occurrences of rollover crashes to transportation agencies.

Examining crash injury severity and barrier-hit outcomes from cable barriers and strong-post guardrails on Alabama's interstate highways

INTRODUCTION

This study investigates the impact of several risk factors (i.e., roadway, driver, vehicle, environmental, and barrier-specific characteristics) on the injury severity resulting from barrier-related crashes and also on barrier-hit outcomes (i.e., vehicle containment, vehicle redirection, and barrier penetration). A total of 1,685 barrier-related crashes, which occurred on three major interstate highways (I-65, I-85, and I-20) in the state of Alabama, were collected for a seven-year period (2010-2016), and all relevant information from the police reports was reviewed. Features that were rarely explored before (e.g., median width, barrier length, barrier offset or lateral position, left shoulder width, blockout type, and number of cables) were also collected and examined. Two types of longitudinal barriers were analyzed: high-tension cable barriers installed on medians and strong-post guardrails installed on medians and/or roadsides.

METHOD

Two separate mixed logit (MXL) models were used to analyze crash injury severity in median and roadside barrier-related crashes. Two additional MXL models were separately adopted for median and roadside barrier-related crashes to estimate the probability of three barrier-hit outcomes (vehicle containment, vehicle redirection, and barrier penetration).

RESULTS

The results of crash injury severity MXL models showed that, for both median and roadside barrier crashes, barrier penetration, female drivers, and driver fatigue were associated with a higher probability of injury or fatal crashes. The results of barrier-hit MXL models showed that longer barrier length, Brifen cable barrier system, and barrier lateral position were significant predictors of median barrier-hit outcomes, whereas dark lighting condition, driving under the influence (DUI), presence of curved freeway sections, and right shoulder width significantly contributed to roadside barrier-hit outcomes.

CONCLUSIONS

The MXL model succeeded in identifying several contributing factors of crash severity and barrier-hit outcomes along Alabama's interstate highways. Practical applications: One study application is to design longer barrier run length (greater than 1230 feet or 0.2 miles) to reduce the barrier penetration likelihood.

A correlated random parameters approach to investigate large truck rollover crashes on mountainous interstates

Rollover risk on mountainous interstates is a major concern for transportation agencies due to the combined mixed effects of adverse weather conditions and complex topography. Such crashes incur hazardous consequences on road users' lives. Therefore, a correlated random parameters logit modeling framework was employed to investigate the influences of crash precursors on rollover risk to identify effective safety countermeasures. This approach was selected to account for both the crash contributing factors' unobserved heterogeneity effects and the correlations among those effects. The data, used in this study, were those of single-truck crashes on Wyoming's interstate curved sections. The traditional logit and uncorrelated random parameters, or mixed, logit models were attempted as well. With that, the analysis results indicated that the fit of the correlated random parameters logit model was superior to those of the others. It also revealed insights regarding correlations among random parameters that were obscure in the other models. According to its results, on average, veering off the road, overcorrections and severe winds raised the risk of single-truck rollover crashes. On the other hand, median barriers, roadside guardrails, tight horizontal curves, icy road surfaces, wet surfaces and surfaces covered by loose material, in general, reduced the likelihood of rollovers. Correlations, such as those between severe winds and icy surfaces and those between roadside guardrails and icy surfaces, were inferred as well. This study's results will assist transportation officials in efficiently identifying appropriate countermeasures to mitigate the impact of truck rollovers particularly during adverse weather conditions.

Injury severity analysis of rollover crashes for passenger cars and light trucks considering temporal stability: A random parameters logit approach with heterogeneity in mean and variance

PROBLEM

The rollover crash is a serious crash type that often causes higher injury severities. Moreover, factors that contribute to the injury severities of rollover crashes may show instabilities in different vehicle types and time periods, which requires further investigations. This study utilizes the rollover crash data in North Carolina from Highway Safety Information System (HSIS) to study the effect instabilities of factors in vehicle type and time periods in rollover crashes.

METHODS

The injury severities of drivers are estimated using the random parameters logit (RPL) model with heterogeneity in means and variances. Available factors in HSIS have been categorized into three groups, which are drivers, road, and environment, respectively. This study also justifies the segmentations through transferability tests. The effects of identified significant factors are evaluated using marginal effects.

RESULTS

Factors such as FWP (farm, wood, and pasture areas), unhealthy physical condition, impaired physical condition, road adverse, and so forth have shown instabilities in marginal effects among vehicle types and time periods. Practical Applications: The finding of this research could provide important references for policy makers and automobile manufactures to help mitigate the injury severity of rollover crashes.

A simulation based large bus side slip and rollover threshold study in slope-curve section under adverse weathers

To study the side slip and rollover threshold of large bus in slope-curve section under adverse weather, factors that affect the safety of large buses that run in slope-curve section, such as rain, snow, cross-wind environmental factors, and road geometry, were analyzed to obtain the friction coefficient of the road surface under different rainfall and snowfall intensities through field measurements and to determine the six-component force coefficient of wind that acts on large buses through wind tunnel tests. The force analysis of large bus in slope-curve section was carried out, and the mechanical equations of large bus under the limit conditions of sideslip and rollover in slope-curve section were established. TruckSim simulation test platform was used to establish a three-dimensional road model and large bus mechanical model at a design speed of 100 km/h. Input parameters, such as cross-wind speed and road friction coefficient, simulate the impact of wind-rain/snow coupling. Under the combined action of wind-rain/snow, the operation test of large bus in slope-curve section was carried out, and the key parameters and indicators of the sideslip and rollover of large bus in slope-curve section were outputted and analyzed. The sudden change point of lateral acceleration is the judging condition for sideslip of large bus in slope-curve section under different road friction coefficient (0.2-0.7), changing from 0.15m/s2 and stabilizing to 0.52 m/s2, and a 0N vertical reaction force of the inner tire is the critical judging condition for rollover under road friction coefficient0.8, and the operating speed thresholds were proposed under different road friction coefficient. This study is expected to provide theoretical support for the speed limit of large bus in slope-curve section under adverse weather.

Rule discovery to identify patterns contributing to overrepresentation and severity of run-off-the-road crashes

The main objective of this paper was to analyse the roadway, environmental, and driver-related factors associated with an overrepresentation of frequency and severity of run-off-the-road (ROR) crashes. The data used in this study refer to the 6167 crashes occurred in the section Naples-Candela of A16 motorway, Italy in the period from 2001 to 2011. The analysis was carried out using the rule discovery technique due to its ability of extracting knowledge from large amounts of data previously unknown and indistinguishable by investigating patterns that occur together in a given event. The rules were filtered by support, confidence, lift, and validated by the lift increase criterion. A two-step analysis was carried out. In the first step, rules discovering factors contributing to ROR crashes were identified. In the second step, studying only ROR crashes, rules discovering factors contributing to severe and fatal injury (KSI) crashes were identified. As a result, 94 significant rules for ROR crashes and 129 significant rules for KSI crashes were identified. These rules represent several combinations of geometric design, roadside, barrier performance, crash dynamic, vehicle, environmental and drivers' characteristics associated with an overrepresentation of frequency and severity of ROR crashes. From the methodological point of view, study results show that the a priori algorithm was effective in providing new information which was previously hidden in the data. Finally, several countermeasures to solve or mitigate the safety issues identified in this study were discussed. It is worthwhile to observe that the study showed a combination of factors contributing to the overrepresentation of frequency and severity of ROR crashes. Consequently, the implementation of a combination of countermeasures is recommended.

Investigation of factors contributing to injury severity in single vehicle motorcycle crashes in India

Single-vehicle crashes are of major concern in both developed as well as in Low Middle Income Countries due to the severity of injuries, particularly fatal accidents. In India, a significant proportion of crashes are single-vehicle crashes. The vehicles which are involved in accidents due to causes such as self skidding, hitting stationary objects, trees that are simply contributed by the drivers themselves are referred to as out-of-control single-vehicle crashes. The main objective of this study is to evaluate the risk factors associated with single-vehicle motorcycle collisions in Tamilnadu, India and identifies the unique characteristics and injury outcomes associated with these collisions. Crash dataset for the present study was prepared from the police-reported crashes for the past nine years that occurred within the state of Tamilnadu between 2009 and 2017. The identified contributory factors which influence injury severity include driver characteristics, crash-related factors, traffic-related factors, vehicle and environment-related factors. In this study, injury severity is classified into three categories, i.e. fatal, serious, and minor injuries. Since the outcome of the injury severity could be measured on an ordinal scale, a discrete ordered outcome model, an ordered logit model is applied. To summarise the results, thirteen of the studied factors are found to have a significant influence on the injury severity of drivers. Results show that the likelihood of fatal injuries increases in crashes where motorcyclists hit stationary fixed objects, hit trees, ran-off road, inclement weather conditions, urban areas. It is also found that winter season, north districts of Tamilnadu, single and two-lane roads, highways, village roads and, other district roads, daylight conditions, drivers who are younger and working-age group, overtaking from left, taking u-turn are associated with less likelihood of fatal crashes. To increase the overall safety of the roads, targeted countermeasures may be designed in light of injury severity of the drivers with respect to single-vehicle crashes also. This study provides useful insights for reducing injury severity in single-vehicle motorcycle crashes.

Incorporating accident liability into crash risk analysis: A multidimensional risk source approach

In the field of traffic safety, the occurrence of accidents remains a cause of concern for road regulators as well as users. Exploring risk factors inducing the accidents and quantifying the accident risk will not only benefit the prevention and control of traffic accidents but also assist in developing effective risk propagation model for road accidents. This study uses detailed accident record data to mine the risk factors affecting the occurrence of accidents, and quantify the accident risk under the combination of risk factors. First, by reviewing relevant literature and analyzing historical accident, we construct a multi-dimension characterization framework of risk factors with bi-level structure. The Human Factors Analysis and Classification System (HFACS) is applied to supplement and improve the framework. Next, under this framework, we identify the risk factors in traffic accident record, and analyze the statistical characteristics from the level of risk sources and risk characteristics. Then, the concept of accident liability weight is proposed to measure the impact of risk factors on accident occurrence. Through the liability affirmation of risk factors, the accident probability are updated. Last, we establish an accident risk quantify model (ARQM) based on the mean mutual information to compare the likelihood of accidents in different scenarios. In addition, we compare the accident probability and risk under equivalent liability and liability affirmation, as well as give some fundamental ideas regarding how to effectively prevent accidents.

Hierarchical binary logit model to compare driver injury severity in single-vehicle crash based on age-groups

Most of the previous single-vehicle crash analysis studies ignored the effect of road-segments level at higher plan that could probably be unobserved heterogeneity and vary among crash-level factor from one road-segment to next and possibly could lead to a potential biased estimated result. This study developed a hierarchical binary logit model which have the ability to account for both unobserved heterogeneity and correlation within road-segment, to investigate and compare the impact of significant factors influencing fatal single-vehicle crash between young, mid-age and old driver model. A seven-years from 2011 to 2017 crash data, Department of Highway (DOH), Thailand were used in this study. The Intra-Class-Correlation values indicate the importance of road-segment level that 10.1%, 12.2% and 12.8% of the total variation were accounted by random effect from road-segment heterogeneity for young, mid-age and old driver model, respectively. The estimated result of this study shows that influence of alcohol and fatigue increase risk of fatal crash among young and old driver, seatbelt-usage reduce risk of being fatal among mid-age and old driver, roadside safety feature (guardrail) significantly reduce fatality risk among young and mid-age driver, and night time driving without light increase probability of fatal crash for mid-age driver. This study recommends the need to enforce the law on driver under influence of alcohol and seatbelt usage, educational campaign on driving, and installation of guardrail on curve road.

Public preference toward an energy transition policy: the case of South Korea

South Korea is pursuing an energy transition policy (ETP) of expanding the use of renewable energy and natural gas and decreasing that of nuclear energy and coal in total generation. An investigation of whether the public is for or against the ETP is needed by the government. This article explores the public preference toward the ETP employing the data collected through a survey of 1000 individuals. They were asked to reveal their preference for the ETP on a five-point scale in the survey. Overall, there are more advocates of the ETP than those who opposed it. For the purpose of analyzing the determinants of advocacy and opposition of the ETP, an ordered probit model is employed. The results suggest that people living in the Seoul Metropolitan area think that the environment is more important than new jobs, or know the renewable energy 100% campaign before the survey is more favorable to the ETP than others. However, people who use electricity for heating tend to be negative about the ETP. As the age increases, people approve of the ETP, but when age goes beyond a certain level, they oppose it.

Identifying the Factors That Increase the Probability of an Injury or Fatal Traffic Crash in an Urban Context in Jordan

The lack of robust studies carried out on urban roads in developing countries makes it difficult to enhance traffic safety, ensuring sustainable roads and cities. This study analyzes the contribution of a number of explanatory variables behind crashes involving injuries on arterial roads in Irbid (Jordan). Five binary logistic regression models were calibrated for a crash dataset from 2014–2018: one for the full database, and the others for the four main crash causes identified by Jordanian Traffic Police reports. The models show that whatever the crash cause, the three most significant factors linked to an injury or fatality lie in urban road sections that are in large-scale neighborhood areas, have fewer than six accesses per kilometer, and have a low traffic volume (under 500 veh/h/ln). Some of these results agree with previous studies in other countries. Jordan’s governmental agencies concerned with urban road safety might use these results to develop appropriate plans and implement priority actions for each crash cause, in addition to undertaking further research for comparative purposes.

Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways

Mountainous highways suffer from high crash rates and fatality rates in many countries, and single-vehicle crashes are overrepresented along mountainous highways. Route familiarity has been found greatly associated with driver behaviour and traffic safety. This study aimed to investigate and compare the contributory factors that significantly influence the injury severities of the familiar drivers and unfamiliar drivers involved in mountainous highway single-vehicle crashes. Based on 3037 cases of mountainous highway single-vehicle crashes from 2015 to 2017, the characteristics related to crash, environment, vehicle and driver are included. Random-effects generalized ordered probit (REGOP) models were applied to model injury severities of familiar drivers and unfamiliar drivers that are involved in the single-vehicle crashes on the mountainous highways, given that the single-vehicle crashes had occurred. The results of REGOP models showed that 8 of the studied factors are found to be significantly associated with the injury severities of the familiar drivers, and 10 of the studied factors are found to significantly influence the injury severities of unfamiliar drivers. These research results suggest that there is a large difference of significant factors contributing to the injury severities between familiar drivers and unfamiliar drivers. The results shed light on both the similar and different causes of high injury severities for familiar and unfamiliar drivers involved in mountainous highway single-vehicle crashes. These research results can help develop effective countermeasures and proper policies for familiar drivers and unfamiliar drivers targetedly on the mountainous highways and alleviate injury severities of mountainous highway single-vehicle crashes to some extent. Based on the results of this study, some potential countermeasures can be proposed to minimize the risk of single-vehicle crashes on different mountainous highways, including tourism highways with a large number of unfamiliar drivers and other normal mountainous highways with more familiar drivers.

Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50^th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

A Bayesian modeling framework for crash severity effects of active traffic management systems

Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. This paper used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities. The estimation results revealed lower severities with ATM deployment. Marginal effects for ATM deployments that featured hard shoulder running (HSR) revealed lower likelihoods for severe and moderate injury crashes of 15.9 % and for minor injury crashes of 10.1 %. The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 % and 8.33 % with ATM without HSR. The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models, revealing better performance with validation data. These results have implications for improving freeway crash risk at critical locations.

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.

The impact of roadway conditions towards accident severity on federal roads in Malaysia

The fatal accidents on the roads remain a global concern. Daily, approximately 18 traffic accidents occur in the Peninsular Malaysia that cause on an average one death in every hour, a situation that needs preventive measures. The development of the effective strategies to reduce such fatal accidents requires the identification of various risk factors including the road condition. We identified such accident severity issues using the public work and police department databases that consisted of 1067 cases of various severity levels occurred on the Malaysian federal roads during 2008 to 2015. These records were used to develop ordered logistic regression model for the accident severity and nine variables were analyzed. The results revealed that the presence of poor horizontal alignment affected the model outcomes. The likelihood of the more serious accident severity due to the poor horizontal alignment was correspondingly about 0.4 times less compared to the absence of such factors. It is established that the present findings may assist the local authorities to take proactive actions to prevent serious road accidents on the road segments possessing the standard horizontal alignment.

The Influence of Road Geometry on Vehicle Rollover and Skidding

This paper analyzes the influence of single and combined unfavorable road geometry on rollover and skidding risks of D-class mid-sized sport utility vehicles (SUVs) with front-wheel drive for roads with design speeds at 80 km/h. A closed-loop simulation model of human-vehicle-road interactions is established to examine the systematic influence of road geometry on vehicle rollover and skidding. The effects of different road geometry on rollover and skidding on SUVs are studied for pavement surface with good and poor friction when vehicles are in the action of steady state cornering. The rollover and skidding risks of the most unfavorable road segments are assessed. The critical wheel is defined by the threshold of skidding during curve negotiation. The results found that SUVs are not easy to rollover on the most unfavorable roads, regardless of good or poor friction of pavement surface. The safety margin of rollover is greater than that of skidding. The safety margin of skidding is minimal on poor friction roads. Therefore, for the sake of driving safety, it is not recommended to design the roads with these unfavorable road geometry combinations.

Severity analysis for large truck rollover crashes using a random parameter ordered logit model

Large truck rollover crashes present significant financial, industrial, and social impacts. This paper presents an effort to investigate the contributing factors to large truck rollover crashes. Specific focus was placed on exploring the role of heterogeneity and the potential sources of heterogeneity regarding their impacts on injury-severity outcomes. The data used in this study contained large truck rollover crashes that occurred between 2007 and 2016 in the state of Florida. A random parameter ordered logit (RPOL) model was applied. Various driver, vehicle, roadway, and crash attributes were explored as potential predictors in the model. Their impacts were examined for the presence of heterogeneity. Interaction effects were then added to the random variables in order to detect potential sources of heterogeneity. Model results showed that the impacts of lighting conditions and driving speed had significant variation across observations, and this variation could be attributed to driver actions and driver conditions at the time of the crash, as well as driver vision obstruction. Findings from this study shed light on the direction, magnitude, and randomness of the factors that contribute to large truck rollover crashes. Findings associated with heterogeneity could help develop more effective and targeted countermeasures to improve freight safety. Driver education programs could be planned more efficiently, and advisory and warning signs could be designed in a more insightful manner by taking into account specific roadway attributes, such as sandy surfaces, downhill, curved alignment, unpaved shoulders, and lighting conditions.

Applying the colocation quotient index to crash severity analyses

Examining the spatial relationships among crashes of various severity levels is essential for gaining a better understanding of the severity distribution and potential contributing factors to collisions. However, relatively few scholars have focused on analyzing this type of data. Therefore, in this study, we utilized a new index, the colocation quotient, to measure the spatial associations among crashes of various severities that occurred in College Station, Texas. This new method has been widely used to define the colocation pattern of categorized data in various fields, but it has not yet been applied to crash severity data. According to our findings, (1) crashes tended to be at the same injury level as those of neighboring ones, which was most significant for fatal crashes and second most significant for non-injury crashes; (2) the colocation quotient matrix tended to be symmetrical in non-injury crashes versus injury crashes (minor injury, major injury, and fatal); and, (3) DWIs (driving while intoxicated) and hit-and runs did not show a strong pattern. These colocation quotient results could be helpful for predicting crash severity and by providing traffic engineers with more effective traffic safety measures.

The priority setting of factors affecting a crash severity using the Analytic Network Process

Background:

The original step in reducing crash severity is recognition of the involved factors. The aim of this paper is to prioritize the factors affecting crashes severity. The current study was carried out in 2018 in Iran.

Methods:

The present cross-sectional study focuses on factors affecting the crash severity. Due to the compli-cated nature of traffic accidents, Multi-Criteria Decision-Making methods can be considered as an effective approach. In this work, the factors affecting a crash severity were identified and then attained factors were scored by ten traffic safety experts. To prioritize and weigh these factors, the Analytic Network Process method and Super Decisions program were used.

Results:

The results showed four main factors and 60 sub-factors in which the main factors in the order of priority were the safety (the most important sub-factor: speed over the upper limit), the other fac-tors (the most important sub-factor: road user type), the health (the most important sub-factor: drowsiness), and the environment (the most important sub-factor: slipping the road).

Conclusions:

In order to control the crash severity, the presented factors in this study could help traffic safety experts to prioritize and perform controlling actions.

Comparison of univariate and two-stage approaches for estimating crash frequency by severity-Case study for horizontal curves on two-lane rural roads

The Highway Safety Manual (HSM) procedures apply specific safety performance functions (SPFs) and crash modification factors (CMFs) appropriate for estimating the safety effects of design and operational changes to a roadway. Although the applicability of the SPFs and CMFs may significantly vary by crash severity, they are mainly based on total crash counts, with different approaches for estimation of crashes by crash severity. The variety of approaches in the HSM and in the literature in general suggests that there may be no one best approach for all situations, and that there is a need to develop and compare alternative approaches for each potential application. This paper addresses this need by demonstrating the development and comparison of alternative approaches using horizontal curves on two-lane roads as a case study. This site type was chosen because of the high propensity for severe crashes and the potential for exploring a wide range of variables that affect this propensity. To facilitate this investigation, a two-stage modeling approach is developed whereby the proportion of crashes for each severity level is estimated as a function of roadway-specific factors and traffic attributes and then applied to an estimate of total crashes from an SPF. Using Highway Safety Information System (HSIS) curve data for Washington state, a heterogeneous negative binomial (HTNB) regression model is estimated for total crash counts and then applied with severity distribution functions (SDFs) developed by a generalized ordered probit model (GOP). To evaluate the performance of this two-stage approach, a comparison is made with predictions directly obtained from estimated univariate SPFs for crash frequency by severity and also from a fixed proportion method that has been suggested in the HSM. The results revealed that, while the two-stage SDF approach and univariate approach adopt different procedures for model estimation, their prediction accuracies are similar, and both are superior to the fixed proportion method. In short, this study highlights the potential of the two-stage SDF approach in accounting for crash frequency variations by severity levels, at least for curved two-lane road sections, and especially for the all too frequent cases where samples are too small to estimate viable univariate crash severity models.

Factors affecting the injury severity of out-of-control single-vehicle crashes in Singapore

Single-vehicle (SV) crashes are of major concerns because of their high fatality rates. To understand the proneness of high injury severity for vehicle operators brought about by SV crashes without the confounding influence of other road users, this study focuses on those SV crashes without colliding with pedestrians, which are defined as out-of-control SV crashes given the general consequence of involved vehicles. Moreover, to compare the influence of contributory factors (including driver-vehicle/rider-vehicle, roadway, and environmental characteristics) by vehicle types, the injury severity for riders of motorized two-wheelers and drivers of other motorized vehicles are investigated separately using two disaggregated ordered probit models. The results show that for both riders and drivers, variables such as age (65 and above), drink driving, error type of failing to have proper control, driving maneuvers of left and right turns as well as driving after midnight are associated with more severe injuries whereas factors such as wet, oily or sandy surfaces are related to less severe injury. Four other variables, i.e., foreign vehicle registration, probation or expired license, high speed-limit roads, and type of median lane, have different influences on riders and drivers on injury severity. Additionally, factors such as road traffic type and nationality are only found to significantly influence only riders and drivers respectively. The results shed light on both the similar and different causes of high injury severity for riders and drivers involved in out-of-control SV crashes. Based on the findings, targeted countermeasures may be introduced from multiple perspectives such as driver education and policy development to improve non-traffic-interactive safety.

The Effects of the RCS’s Application in the Value Added Tax Collecting Process on the Perception of SME Taxpayer in Korea’s Trade Activity: Transparency and Fairness in Trade

Value Added Tax (VAT) is the largest source of global tax revenue. However, it faces issues of tax avoidance, such as the black-market and missing trader intra-community fraud. Many studies have postulated that the introduction of the Reverse Charge System (RCS) will contribute to the enhancement of transparency and fairness in trade for small and medium enterprises (SMEs) that supply goods or provide services. This study analyzes SME taxpayers’ perception of transparency and fairness in trade resulting from the introduction of the RCS into the South Korean VAT system. In B2B transactions that handle gold, copper, and steel scrap, large companies often abuse their bargaining power over smaller firms by not paying VAT with their purchase or reducing the prices to equal VAT, leading to low trade transparency. When it comes to gold, copper and steel scrap trading, the imbalance in bargaining power results in one of the parties being unable to reap maximum benefits because of unfairness. SMEs with relatively weak bargaining power suffer from unfair trading practices such as price cutting and the imposition of VAT by their counterparts. Since the introduction of the RCS, however, SME taxpayers appear to believe that trade fairness has improved. This study’s findings reveal that SMEs are more likely to perceive improvement in trade transparency and fairness since the implementation of the RCS, indicating that the RCS has exerted a positive influence on SMEs. This study provides important policy implications for countries that intend to implement the RCS by offering empirical evidence of its benefits.

Taxpayer’s Religiosity, Religion, and the Perceptions of Tax Equity: Case of South Korea

The purpose of this study is to investigate the effect of religiosity and religion on tax equity. Most prior studies have argued that higher taxpayers’ religiosity reduces tax evasion and increases the level of tax morale. Various studies have also shown that tax evasion and morale vary with perceptions of tax equity, including exchange, horizontal, and vertical equities. However, the relationship between religiosity and tax equity has not been studied actively. Especially in Korea, there has been considerable debate about the implementation of taxation for clergies. Therefore, the relationship between religiosity and tax equity will be analyzed clearly using Korean survey panel data. The results of this study show that religiosity and religion do not affect exchange and horizontal equity; however, each religion affects vertical equity. This implies that economic and social incentives are more effective than religiosity and religion on taxpayers’ tax evasion or morale.

Injury severity analysis of commercially-licensed drivers in single-vehicle crashes: Accounting for unobserved heterogeneity and age group differences

This study analyzes the injury severity of commercially-licensed drivers involved in single-vehicle crashes. Considering the discrete ordinal nature of injury severity data, the ordered response modeling framework was adopted. The moderating effect of driver's age on all other factors was examined by segmenting the parameters by driver's age group. Additional effects of the different drivers' age groups are taken into consideration through interaction terms. Unobserved heterogeneity of the different covariates was investigated using the Mixed Generalized Ordered Response Probit (MGORP) model. The empirical analysis was conducted using four years of the Highway Safety Information System (HSIS) data that included 6247 commercially-licensed drivers involved in single-vehicle crashes in the state of Minnesota. The MGORP model elasticity effects indicate that key factors that increase the likelihood of severe crashes for commercially-licensed drivers across all age groups include: lack of seatbelt usage, collision with a fixed object, speeding, vehicle age of 11 years or more, wind, night time, weekday, and female drivers. Also, the effects of several covariates were found to vary across different age groups.

Traffic accident severity analysis with rain-related factors using structural equation modeling - A case study of Seoul City

Weather conditions are strongly correlated with traffic accident severity. In particular, rain-related factors are an important cause of traffic accidents due to the poor visibility and reduced friction resulting from slippery road conditions. This paper presents a systematic approach to analyze the extent to which the rainfall intensity and level of water depth are responsible for traffic accidents using Seoul City, Korea, as a case study. The rainfall and traffic accident data over a nine-year period (from 2007 to 2015) for Seoul were analyzed through Structural Equation Modeling to identify the relationships among variables by handling endogenous and exogenous variables simultaneously. In the model, four latent variables, namely those representing the road; traffic, environmental, and human factors; and rain and water depth factors, were defined and the coefficients of the latent, endogenous, and exogenous variables were estimated to obtain the level of accident severity. Furthermore, a statistical goodness of fit index was suggested for model fitting. In conclusion, traffic, environmental, and human factors; rain and water depth factors; and road factors are mutually correlated with the level of accident severity. Compact cars, young drivers, female drivers, heavy rain, deep water, and roads with a long drainage length are more likely to be associated with an increase in the level of accident severity, as are features like a tangent, down slope, right-hand curve, and shorter curve length.