Analysis of the injury severity of crashes by considering different lighting conditions on two-lane rural roads

IntroductionMany studies have examined different factors contributing to the injury severity of crashes; however, relatively few studies have focused on the crashes by considering the specific effects of lighting conditions. This research investigates lighting condition differences in the injury severity of crashes using 3-year (2009–2011) crash data of two-lane rural roads of the state of Washington.MethodSeparate ordered-probit models were developed to predict the effects of a set of factors expected to influence injury severity in three lighting conditions; daylight, dark, and dark with street lights. A series of likelihood ratio tests were conducted to determine if these lighting condition models were justified.ResultsThe modeling results suggest that injury severity in specific lighting conditions are associated with contributing factors in different ways, and that such differences cannot be uncovered by focusing merely on one aggregate model. Key differences include crash location, speed limit, shoulder width, driver action, and three collision types (head-on, rear-end, and right-side impact collisions).Practical ApplicationsThis paper highlights the importance of deploying street lights at and near intersections (or access points) on two-lane rural roads because injury severity highly increases when crashes occur at these points in dark conditions.     

Efficacies of roadway safety improvements across functional subclasses of rural two-lane highways

Introduction

Highway crash occurrence is a leading cause of unnatural deaths, and highway agencies continually seek to identify engineering measures to reduce crashes and to assess the efficacy of such measures. Most past studies on the effectiveness of roadway improvements in terms of crash reduction considered all rural two-lane sections as a single category of roads. However, it may be hypothesized that the differences in the mobility and accessibility characteristics that are reflected in (and due to) the different design standards between different functional subclasses in the rural two-lane highway system can lead to differences in efficacies of safety improvements at these subclasses. This paper investigates the efficacy of roadway improvements, in terms of crash reduction, at the various subclasses of rural two-lane highways.

Methods

An empirical analysis of safety performance at each of the three subclasses of rural two-lane highways was carried out using the negative binomial modeling technique. For each subclass, crash prediction models were developed separately for the three levels of crash severity: property-damage only, injury, and fatal/injury. The crash factors that were considered include lane width, shoulder width, pavement surface friction, pavement condition, and horizontal and vertical alignments. After having developed the safety performance functions, the effectiveness (in terms of the extent of crash reduction, for different levels of crash severity) of highway safety enhancements at each highway subclass were determined using the theoretical concepts established in past literature. These enhancements include widening lanes, widening shoulders, enhancing pavement surface friction, and improving the vertical or horizontal alignment.

Results and Conclusion

The study found that there is empirical evidence to justify the decomposition of the family of rural two-lane roads into its constituent subclasses for purposes of analyzing the effectiveness of safety enhancement projects and thus to avoid underestimation or overestimation of benefits of safety improvements at this class of highways.     

Older Adults at Increased Risk as Pedestrians in Victoria,Australia: An Examination of Crash Characteristics and Injury Outcomes

Objectives: Engaging in active transport modes (especially walking) is a healthy and environmentally friendly alternative to driving and may be particularly beneficial for older adults. However, older adults are a vulnerable group: they are at higher risk of injury compared with younger adults, mainly due to frailty and may be at increased risk of collision due to the effects of age on sensory, cognitive, and motor abilities. Moreover, our population is aging, and there is a trend for the current cohort of older adults to maintain mobility later in life compared with previous cohorts. Though these trends have serious implications for transport policy and safety, little is known about the contributing factors and injury outcomes of pedestrian collision. Further, previous research generally considers the older population as a homogeneous group and rarely considers the increased risks associated with continued ageing.

Method: Collision characteristics and injury outcomes for 2 subgroups of older pedestrians (65–74 years and 75+ years) were examined by extracting data from the state police–reported crash dataset and hospital admission/emergency department presentation data over the 10-year period between 2003 and 2012. Variables identified for analysis included pedestrian characteristics (age, gender, activity, etc.), crash location and type, injury characteristics and severity, and duration of hospital stay. A spatial analysis of crash locations was also undertaken to identify collision clusters and the contribution of environmental features on collision and injury risk.

Results: Adults over 65 years were involved in 21% of all pedestrian collisions. A high fatality rate was found among older adults, particularly for those aged 75 years and older: this group had 3.2 deaths per 100,000 population, compared to a rate of 1.3 for 65- to 74-year-olds and 0.7 for adults below 65 years of age. Older pedestrian injuries were most likely to occur while crossing the carriageway; they were also more likely to be injured in parking lots, at driveway intersections, and on sidewalks compared to younger cohorts. Spatial analyses revealed older pedestrian crash clusters on arterial roads in urban shopping precincts. Significantly higher rates of hospital admissions were found for pedestrians over the age of 75 years and for abdominal, head, and neck injuries; conversely, older adults were underrepresented in emergency department presentations (mainly lower and upper extremity injuries), suggesting an increased severity associated with older pedestrian injuries. Average length of hospital stay also increased with increasing age.

Conclusion: This analysis revealed age differences in collision risk and injury outcomes among older adults and that aggregate analysis of older pedestrians can distort the significance of risk factors associated with older pedestrian injuries. These findings have implications that extend to the development of engineering, behavioral, and enforcement countermeasures to address the problems faced by the oldest pedestrians and reduce collision risk and improve injury outcomes.     

Gender differences of young drivers on injury severity outcome of highway crashes

ProblemGender differences of young drivers involved in crashes and the associated differences in risk factors have not been fully explored in the United States (U.S.). Accordingly, this study investigated the topic, where the odds ratios (ORs) were used to identify differences in crash involvements between male and female young drivers.MethodLogistic regression models for injury severity of young male drivers and young female drivers were developed. Different driver, environmental, vehicle, and road related factors that have affected young female drivers' and young male drivers' crash involvements were identified using the models.ResultsResults indicated that some variables are significantly related to female drivers' injury risk but not male drivers' injury risk and vice versa. Variables such as driving with valid licenses, driving on weekends, avoidance or slow maneuvers at time of crash, non-collision and overturn crashes, and collision with a pedestrian were significant variables in female driver injury severity model but not in young male driver severity model. Travel on graded roadways, concrete surfaces, and wet road surfaces, collision with another vehicle, and rear-end collisions were variables that were significant in male-driver severity model but not in female-driver severity model.SummaryFactors which increase young female drivers' injury severity and young male drivers' injury severity were identified. This study adds detailed information about gender differences and similarities in injury severity risk of young drivers.Practical applicationsIt is important to note that the findings of this study show that gender differences do exists among young drivers. This sends a message to the industry that the transportation professionals and researchers, who are developing countermeasures to increase the traffic safety, may need to pay attention to the differences. This might be particularly true when developing education materials for driver training for young/inexperienced drivers.     

Associations of distraction involvement and age with driver injury severities

IntroductionThis paper investigates the associations between the severity of injuries sustained by a driver who is involved in a two-vehicle crash, the existence and type of driver distraction as well as driver's age. Few studies investigated distraction as it relates to injury severity. Moreover, these studies did not consider driver age which is a significant factor related to driving behavior and the ability to respond in a crash situation.MethodsAn ordered logit model was built to predict injury severity sustained by drivers using data from the U.S. National Automotive Sampling System's General Estimates System (2003 to 2008). Various factors (e.g., weather, gender, and speeding) were statistically controlled for, but the main focus was on the interaction of driver age and distraction type.ResultsThe trends observed for young and mid-age drivers were similar. For these age groups, dialing or texting on the cell phone, passengers, and in-vehicle sources resulted in an increase in a likelihood of more severe injuries. Talking on the cell phone had a similar effect for younger drivers but was not significant for mid-age drivers. Inattention and distractions outside the vehicle decreased the odds of severe injuries. For older drivers, the highest odds of severe injuries were observed with dialing or texting on a cell phone, followed by in-vehicle sources and talking on the cell phone. All these sources were associated with an increased likelihood of injury severity. Similar to young and mid-age drivers, distractions outside the vehicle decreased the odds of severe injuries. Other distraction types did not have a significant effect for the older age group.ConclusionsThe results support previous literature and extend our understanding of crash injury severity.Practical applicationsThe findings have implications for policy making and the design of distraction mitigation systems.     

Secondary collisions and injury severity: A joint analysis using structural equation models

Objective: This study aims to investigate the contributing factors to secondary collisions and the effects of secondary collisions on injury severity levels. Manhattan, which is the most densely populated urban area of New York City, is used as a case study. In Manhattan, about 7.5% of crash events become involved with secondary collisions and as high as 9.3% of those secondary collisions lead to incapacitating and fatal injuries.

Methods: Structural equation models (SEMs) are proposed to jointly model the presence of secondary collisions and injury severity levels and adjust for the endogeneity effects. The structural relationship among secondary collisions, injury severity, and contributing factors such as speeding, alcohol, fatigue, brake defects, limited view, and rain are fully explored using SEMs. In addition, to assess the temporal effects, we use time as a moderator in the proposed SEM framework.

Results: Due to its better performance compared with other models, the SEM with no constraint is used to investigate the contributing factors to secondary collisions. Thirteen explanatory variables are found to contribute to the presence of secondary collisions, including alcohol, drugs, inattention, inexperience, sleep, control disregarded, speeding, fatigue, defective brakes, pedestrian involved, defective pavement, limited view, and rain. Regarding the temporal effects, results indicate that it is more likely to sustain secondary collisions and severe injuries at night.

Conclusions: This study fully investigates the contributing factors to secondary collisions and estimates the safety effects of secondary collisions after adjusting for the endogeneity effects and shows the advantage of using SEMs in exploring the structural relationship between risk factors and safety indicators. Understanding the causes and impacts of secondary collisions can help transportation agencies and automobile manufacturers develop effective injury prevention countermeasures.     

Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data

IntroductionPrior research has shown the probability of a crash occurring on horizontal curves to be significantly higher than on similar tangent segments, and a disproportionally higher number of curve-related crashes occurred in rural areas. Challenges arise when analyzing the safety of horizontal curves due to imprecision in integrating information as to the temporal and spatial characteristics of each crash with specific curves.MethodsThe second Strategic Highway Research Program(SHRP 2) conducted a large-scale naturalistic driving study (NDS),which provides a unique opportunity to better understand the contributing factors leading to crash or near-crash events. This study utilizes high-resolution behavioral data from the NDS to identify factors associated with 108 safety critical events (i.e., crashes or near-crashes) on rural two-lane curves. A case-control approach is utilized wherein these events are compared to 216 normal, baseline-driving events. The variables examined in this study include driver demographic characteristics, details of the traffic environment and roadway geometry, as well as driver behaviors such as in-vehicle distractions.ResultsLogistic regression models are estimated to discern those factors affecting the likelihood of a driver being crash-involved. These factors include high-risk behaviors, such as speeding and visual distractions, as well as curve design elements and other roadway characteristics such as pavement surface conditions.ConclusionsThis paper successfully integrated driver behavior, vehicle characteristics, and roadway environments into the same model. Logistic regression model was found to be an effective way to investigate crash risks using naturalistic driving data.Practical ApplicationsThis paper revealed a number of contributing factors to crashes on rural two-lane curves, which has important implications in traffic safety policy and curve geometry design. This paper also discussed limitations and lessons learned from working with the SHRP 2 NDS data. It will benefit future researchers who work with similar type of data.     

Driver injury severity related to inclement weather at highway–rail grade crossings in the United States

Objective: Previous studies on crash modeling at highway–rail grade crossings were aimed at exploring the factors that are likely to increase the crash frequencies at highway–rail grade crossings. In recent years, modeling driver's injury severity at highway–rail grade crossings has received interest. Because there were substantial differences among different weather conditions for driver's injury severity, this study attempts to explore the impact of weather influence on driver injury at highway–rail grade crossing.

Method: Utilizing the most recent 10 years (2002–2011) of highway–rail grade crossing accident data, this study applied a mixed logit model to explore the determinants of driver injury severity under different weather conditions at highway–rail grade crossing.

Results: Analysis results indicate that drivers' injury severity at highway–rail grade crossings is strongly different for different weather conditions. It was found that the factors significantly impacting driver injury severity at highway–rail grade crossings include motor vehicle speed, train speed, driver's age, gender, area type, lighting condition, highway pavement, traffic volume, and time of day.

Conclusions: The findings of this study indicate that crashes are more prevalent if vehicle drivers are driving at high speed or the oncoming trains are high speed. Hence, a reduction in speed limit during inclement weather conditions could be particularly effective in moderating injury severity, allowing more reaction time for last-minute maneuvering and braking in moments before impacts. In addition, inclement weather-related crashes were more likely to occur in open areas and highway–rail grade crossings without pavement and lighting. Paved highway–rail grade crossings with installation of lights could be particularly effective in moderating injury severity.     

The interactive effect on injury severity of driver-vehicle units in two-vehicle crashes

IntroductionThis study sets out to investigate the interactive effect on injury severity of driver-vehicle units in two-vehicle crashes.MethodA Bayesian hierarchical ordered logit model is proposed to relate the variation and correlation of injury severity of drivers involved in two-vehicle crashes to the factors of both driver-vehicle units and the crash configurations. A total of 6417 crash records with 12,834 vehicles involved in Florida are used for model calibration.ResultsThe results show that older, female and not-at-fault drivers and those without use of safety equipment are more likely to be injured but less likely to injure the drivers in the other vehicles. New vehicles and lower speed ratios are associated with lower injury degree of both drivers involved. Compared with automobiles, vans, pick-ups, light trucks, median trucks, and heavy trucks possess better self-protection and stronger aggressivity. The points of impact closer to the driver's seat in general indicate a higher risk to the own drivers while engine cover and vehicle rear are the least hazardous to other drivers. Head-on crashes are significantly more severe than angle and rear-end crashes. We found that more severe crashes occurred on roadways than on shoulders or safety zones.ConclusionsBased on these results, some suggestions for traffic safety education, enforcement and engineering are made. Moreover, significant within-crash correlation is found in the crash data, which demonstrates the applicability of the proposed model.     

Commercial truck crash injury severity analysis using gradient boosting data mining model

IntroductionTruck crashes contribute to a large number of injuries and fatalities. This study seeks to identify the contributing factors affecting truck crash severity using 2010 to 2016 North Dakota and Colorado crash data provided by the Federal Motor Carrier Safety Administration.MethodTo fulfill a gap of previous studies, broad considerations of company and driver characteristics, such as company size and driver’s license class, along with vehicle types and crash characteristics are researched. Gradient boosting, a data mining technique, is applied to comprehensively analyze the relationship between crash severities and a set of heterogeneous risk factors.ResultsTwenty five variables were tested and 22 of them are identified as significant variables contributing to injury severities, however, top 11 variables account for more than 80% of injury forecasting. The relative variable importance analysis is conducted and furthermore marginal effects of all contributing factors are also illustrated in this research. Several factors such as trucking company attributes (e.g., company size), safety inspection values, trucking company commerce status (e.g., interstate or intrastate), time of day, driver’s age, first harmful events, and registration condition are found to be significantly associated with crash injury severity. Even though most of the identified contributing factors are significant for all four levels of crash severity, their relative importance and marginal effect are all different.ConclusionsFor the first time, trucking company and driver characteristics are proved to have significant impact on truck crash injury severity. Some of the results in this study reinforce previous studies’ conclusions.Practical applicationsFindings in this study can be helpful for transportation agencies to reduce injury severity, and develop efficient strategies to improve safety.     

In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention

IntroductionA pedestrian crash occurs due to a series of contributing factors taking effect in an antecedent-consequent order. One specific type of antecedent-consequent order is called a crash causation pattern. Understanding crash causation patterns is important for clarifying the complicated growth of a pedestrian crash, which ultimately helps recommend corresponding countermeasures. However, previous studies lack an in-depth investigation of pedestrian crash cases, and are insufficient to propose a representative picture of causation patterns. Method: In this study, pedestrian crash causation patterns were discerned by using the Driving Reliability and Error Analysis Method (DREAM). One hundred and forty-two pedestrian crashes were investigated, and five pedestrian pre-crash scenarios were extracted. Then, the crash causation patterns in each pre-crash scenario were analyzed; and finally, six distinct patterns were identified. Accordingly, 17 typical situations corresponding to these causation patterns were specified as well. Results: Among these patterns, the pattern related to distracted driving and the pattern related to an unexpected change of pedestrian trajectory contributed to a large portion of the total crashes (i.e., 27% and 24%, respectively). Other patterns also played an important role in inducing a pedestrian crash; these patterns include the pattern related to an obstructed line of sight caused by outside objects (9%), the pattern that involves reduced visibility (13%), and the pattern related to an improper estimation of the gap distance between the vehicle and the pedestrian (10%). The results further demonstrated the inter-heterogeneity of a crash causation pattern, as well as the intra-heterogeneity of pattern features between different pedestrian pre-crash scenarios. Conclusions and practical applications: Essentially, a crash causation pattern might involve different contributing factors by nature or dependent on specific scenarios. Finally, this study proposed suggestions for roadway facility design, roadway safety education and pedestrian crash prevention system development.     

Contributing factors of crash injury severity at public highway-railroad grade crossings in the U.S.

IntroductionThe Moving Ahead for Progress in the 21st Century (MAP-21) includes a separate program that supports safety improvements to reduce the number of fatalities and injuries at public highway-railroad grade crossings (HRGCs). This study identifies the significant factors affecting crash injury severity at public HRGCs in the United States.MethodCrashes from 2009 through 2013 on 5,528 public HRGCs, extracted from the Federal Railroad Administration database, were used in the analysis. A comprehensive list of risk factors was explored. Examples include predictors related to geographic region of crash, geometry (e.g., area type and pavement marking type), railroad (e.g., warning device type and railroad class), traffic (e.g., train speed and vehicles annual average daily traffic “AADT”), highway user (e.g., driver age and gender), and environment (e.g., lighting and weather conditions). The study used the mixed logit model to better capture the complex highway user behavior at HRGCs.ResultsFemale highway users were at higher risk of involvement in injuries and fatalities compared to males. Higher train speeds, very old drivers, open areas, concrete road surface types, and railroad equipment striking highway users before crash, were all found to increase the injury likelihood. On the other hand, young and middle-age drivers, non-passing of standing vehicles at HRGCs, industrial areas, and presence of warning bells were found to reduce injuries and fatalities.ConclusionsThe mixed logit model succeeded in identifying contributing factors of crash severity at public HRGCs and potential countermeasures to reduce both fatalities and injuries are suggested.Practical applicationsIt is important to install warning bells at public HRGCs, especially at those with high number of injury and fatality crashes. Enforcement of traffic nearby HRGCs is necessary to prevent vehicles from overtaking of standing vehicles.     

Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis

Introduction: Alcohol-related impairment is a key contributing factor in traffic crashes. However, only a few studies have focused on pedestrian impairment as a crash characteristic. In Louisiana, pedestrian fatalities have been increasing. From 2010 to 2016, the number of pedestrian fatalities increased by 62%. A total of 128 pedestrians were killed in traffic crashes in 2016, and 34.4% of those fatalities involved pedestrians under the influence (PUI) of drugs or alcohol. Furthermore, alcohol-PUI fatalities have increased by 120% from 2010 to 2016. There is a vital need to examine the key contributing attributes that are associated with a high number of PUI crashes. Method: In this study, the research team analyzed Louisiana’s traffic crash data from 2010 to 2016 by applying correspondence regression analysis to identify the key contributing attributes and association patterns based on PUI involved injury levels. Results: The findings identified five risk clusters: intersection crashes at business/industrial locations, mid-block crashes on undivided roadways at residential and business/residential locations, segment related crashes associated with a pedestrian standing in the road, open country crashes with no lighting at night, and pedestrian violation related crashes on divided roadways. The association maps identified several critical attributes that are more associated with fatal and severe PUI crashes. These attributes are dark to no lighting, open country roadways, and non-intersection locations. Practical Applications: The findings of this study may be used to help design effective mitigation strategies to reduce PUI crashes.     

Hierarchical ordered model for injury severity of pedestrian crashes in South Korea

Introduction: The high percentage of fatalities in pedestrian-involved crashes is a critical social problem. The purpose of this study is to investigate factors influencing injury severity in pedestrian crashes by examining the demographic and socioeconomic characteristics of the regions where crashes occurred. Method: To understand the correlation between the unobserved characteristics of pedestrian crashes in a defined region, we apply a hierarchical ordered model, in which we set crash characteristics as lower-level variables and municipality characteristics as upper-level. Pedestrian crash data were collected and analyzed for a three-year period from 2011 to 2013. The estimation results show the statistically significant factors that increase injury severity of pedestrian crashes. Results: At the crash level, the factors associated with increased severity of pedestrian injury include intoxicated drivers, road-crossing pedestrians, elderly pedestrians, heavy vehicles, wide roads, darkness, and fog. At the municipality level, municipalities with low population density, lower level of financial independence, fewer doctors, and a higher percentage of elderly residents experience more severe pedestrian crashes. Municipalities ranked as having the top 10% pedestrian fatality rate (fatalities per 100,000 residents) have rates 7.4 times higher than municipalities with the lowest 10% rate of fatalities. Their demographic and socioeconomic characteristics also have significant differences. The proposed model accounts for a 7% unexplained variation in injury severity outcomes between the municipalities where crashes occurred. Conclusion: To enhance the safety of vulnerable pedestrians, considerable investments of time and effort in pedestrian safety facilities and zones should be made. More certain and severe punishments should be also given for the traffic violations that increase injury severity of pedestrian crashes. Furthermore, central and local governments should play a cooperative role to reduce pedestrian fatalities. Practical applications: Based on our study results, we suggest policy directions to enhance pedestrian safety.     

Investigating the risk factors associated with pedestrian injury severity in Illinois

IntroductionPedestrians are known as the most vulnerable road users, which means their needs and safety require specific attention in strategic plans. Given the fact that pedestrians are more prone to higher injury severity levels compared to other road users, this study aims to investigate the risk factors associated with various levels of injury severity that pedestrians experience in Illinois.MethodOrdered-response models are used to analyze single-vehicle, single-pedestrian crash data from 2010 to 2013 in Illinois. As a measure of net change in the effect of significant variables, average direct pseudo-elasticities are calculated that can be further used to prioritize safety countermeasures. A model comparison using AIC and BIC is also provided to compare the performance of the studied ordered-response models.ResultsThe results recognized many variables associated with severe injuries: older pedestrians (more than 65 years old), pedestrians not wearing contrasting clothing, adult drivers (16–24), drunk drivers, time of day (20:00 to 05:00), divided highways, multilane highways, darkness, and heavy vehicles. On the other hand, crossing the street at crosswalks, older drivers (more than 65 years old), urban areas, and presence of traffic control devices (signal and sign) are associated with decreased probability of severe injuries.Conclusions and practical applicationsThe comparison between three proposed ordered-response models shows that the partial proportional odds (PPO) model outperforms the conventional ordered (proportional odds—PO) model and generalized ordered logit model (GOLM). Based on the findings, stricter rules to address DUI driving is suggested. Educational programs need to focus on older pedestrians given the increasing number of older people in Illinois in the upcoming years. Pedestrians should be educated to use pedestrian crosswalks and contrasting clothing at night. In terms of engineering countermeasures, installation of crosswalks where pedestrian activity is high seems a promising practice.     

Analysis of risk factors affecting driver injury and crash injury with drivers under the influence of alcohol (DUI) and non-DUI

Objective: The objective of this research was to study risk factors that significantly influence the severity of crashes for drivers both under and not under the influence of alcohol.

Methods: Ordinal logistic regression was applied to analyze a crash data set involving drivers under and not under the influence of alcohol in China from January 2011 to December 2014.

Results: Four risk factors were found to be significantly associated with the severity of driver injury, including crash partner and intersection type. Age group was found to be significantly associated with the severity of crashes involving drivers under the influence of alcohol. Crash partner, intersection type, lighting conditions, gender, and time of day were found to be significantly associated with severe driver injuries, the last of which was also significantly associated with severe crashes involving drivers not under the influence of alcohol.

Conclusions: This study found that pedestrian involvement decreases the odds of severe driver injury when a driver is under the influence of alcohol, with a relative risk of 0.05 compared to the vehicle-to-vehicle group. The odds of severe driver injury at T-intersections were higher than those for traveling along straight roads. Age was shown to be an important factor, with drivers 50–60 years of age having higher odds of being involved in severe crashes compared to 20- to 30-year-olds when the driver was under the influence of alcohol.

When the driver was not under the influence of alcohol, drivers suffered more severe injuries between midnight and early morning compared to early nighttime. The vehicle-to-motorcycle and vehicle-to-pedestrian groups experienced less severe driver injuries, and vehicle collisions with fixed objects exhibited higher odds of severe driver injury than did vehicle-to-vehicle impacts. The odds of severe driver injury at cross intersections were 0.29 compared to travel along straight roads. The odds of severe driver injury when street lighting was not available at night were 3.20 compared to daylight. The study indicated that female drivers are more likely to experience severe injury than male drivers when not under the influence of alcohol. Crashes between midnight and early morning exhibited higher odds of severe injury compared to those occurring at other times of day.

The identification of risk factors and a discussion on the odds ratio between levels of the impact of the driver injury and crash severity may benefit road safety stakeholders when developing initiatives to reduce the severity of crashes.     

Data mining approach to model bus crash severity in Australia

Introduction: Buses are different vehicles in terms of dimensions, maneuverability, and driver's vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality. Method: Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006–2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted. Results: Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians’ distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas.     

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.