Single-vehicle Crashes Research Articles

Hotspot analysis of single-vehicle lane departure crashes in North Dakota

According to the North Dakota Department of Transportation (NDDOT), 90% of the state's fatal lane departure crashes between 2015 and 2019 occurred on rural roads. Of these, 77% were single-vehicle events. The objective here was to identify relatively high-risk areas on the rural road system. Spatial analysis techniques were explored as a beneficial tool in resource allocations aimed at single-vehicle crash prevention. Hotspot identification techniques, including Global Moran's I, local Moran's I, network kernel density estimation (NetKDE), and emerging hotspot analysis were employed. While the Global Moran's I index indicated the existence of crash clustering, the local Moran's I statistic revealed hot and cold spots in the state. The NetKDE approach was used to quantify crash clusters and prioritize locations. Results from NetKDE defined boundaries for each cluster in terms of density values embedded in the roadway. Emerging hotspot analysis evaluated the hot and cold spots with respect to time. This study will provide valuable insight and help decision makers to make more informed decisions with respect to education, enforcement and infrastructure strategies aimed at preventing single-vehicle lane departure crashes. Although limited to a narrow crash type in one state, this approach can inform other jurisdictions seeking to empirically visualize hotspots and more effectively deploy traffic safety strategies.

Driver, roadway, and weather factors on severity of lane departure crashes in Maine

Introduction: In Maine, lane departure crashes account for over 70% of roadway fatalities. The majority of roadways in Maine are rural. Moreover, Maine has aging infrastructure, houses the oldest population in the United States, and experiences the third coldest weather in the United States. Methods: This study analyzes the impact of roadway, driver, and weather factors on the severity of single-vehicle lane departure crashes occurring from 2017 to 2019 on rural roadways in Maine. Rather than using police reported weather, weather station data were utilized. Four facility types: Interstates, minor arterials, major collectors, and minor collectors were considered for analysis. The Multinomial Logistic Regression model was used for the analysis. The property damage only (PDO) outcome was considered as the reference (or base) category. Results: The modeling results show that the odds of a crash leading to major injury or fatality (KA outcome) increases by 330%, 150%, 243%, and 266% for older drivers (65 or above) compared to young drivers (29 or less) on Interstates, minor arterials, major collectors, and minor collectors, respectively. During the winter period (October to April), the odds of KA severity outcome (with respect to the PDO) decreases by 65%, 65%, 65%, and 48% on Interstates, minor arterials, major collectors, and minor collectors, respectively, presumably due to reduced speeds during winter weather events. Conclusion: In Maine, factors such as older drivers, operating under the influence, speeding, precipitation, and not wearing a seatbelt showed higher odds of leading to injury. Practical Applications: This study provides safety analysts and practitioners in Maine a comprehensive study of factors that influence the severity of crashes in Maine at different facilities to improve maintenance strategies, enhance safety using proper safety countermeasures, or increase awareness across the state.

Factors influencing fatal vehicle-involved crash consequence metrics at spatio-temporal hotspots in South Korea: application of GIS and machine learning techniques

ABSTRACT Studies have employed several techniques to identify the effect of individual risk factors influencing crashes at hotspot locations. Nevertheless, as crashes are sometimes influenced by a combination of risk factors, identifying the chains of factors collectively contributing to fatal crashes at hotspot locations could provide added insights for improving traffic safety. By employing fatal crash data from Korea, this study identifies hotspots with increasing (critical) and decreasing (diminishing) temporal trends using a spatio-temporal hotspot analysis tool in GIS. Further, a machine learning technique is employed to explore the chains of factors influencing the number of vehicles and the number of casualties involved in fatal crashes at intersections and midblocks in each hotspot type identified. In general, results showed that minibuses/vans and construction vehicles were mainly at fault for fatal single-vehicle pedestrian-involved crashes. While many casualties and vehicles are likely to be involved in crashes at midblocks during the daytime regardless of the hotspot type, the nighttime variable was particularly associated with large casualty-size crashes at critical intersection hotspots. Further, while reckless driving was mostly associated with single-vehicle crashes at intersections in diminishing hotspots, pedestrian protection, and improper centreline crossing violations were more pronounced at midblocks in diminishing hotspots. This analysis identified groups of factors that could be collectively controlled to improve road safety and proposed countermeasures to mitigate fatal crashes on roadways.

Light where it matters: IIHS headlight ratings are correlated with nighttime crash rates

Introduction: Vehicle headlights are the primary means of providing visibility illumination for drivers at night, when crash rates are several times higher than during the day. Based on research indicating a wide range of headlight performance in the passenger vehicle fleet and the absence of a comprehensive and objective consumer evaluation program, the Insurance Institute for Highway Safety (IIHS) began testing and rating headlight systems in 2015. The purpose of this study was to examine the relationship between headlight visibility, as quantified by IIHS, and real-world crash occurrence. Methods: Poisson regression was used to estimate the effects of the headlight rating and the underlying demerits on the rate of police-reported nighttime single-vehicle crashes per vehicle mile traveled. Results: Results indicate that vehicles with better headlight visibility have lower nighttime crash rates after controlling for differences in daytime rates and other factors. A reduction of 10 visibility demerits, the equivalent of one overall rating band, was estimated to reduce the nighttime crash rate by 4.6% (95% CI: 2.1%–7.0%). While statistical significance was limited by small sample sizes, good-rated headlights were estimated to reduce crash rates by 12 to 29% relative to those with poor ratings for the different types of single-vehicle crashes studied. Among different components of the IIHS rating, the assessments of low and high beam curve visibility were associated with the greatest crash rate reductions. Conclusions: This study demonstrates that the IIHS evaluation program encourages headlight designs that reduce the risk of nighttime single-vehicle crashes. Practical applications: Headlight systems have a meaningful effect on nighttime crash rates. Drivers can reduce their crash risk by selecting a vehicle with one of the best designs.

Acute Cognitive Dysfunction in a Patient With Traumatic Brain Injury: A Clinical Vignette.

Acute Cognitive Dysfunction in a Patient With Traumatic Brain Injury: A Clinical Vignette.

Traffic crash changes following transitions between daylight saving time and standard time in the United States: New evidence for public policy making

Introduction: This study examined the impact of daylight saving time changes on traffic crashes in the United States. Using a data-driven regression analysis approach, the study analyzed 2014–2016 crash data from six states spanning all four major time zones in the contiguous United States. Method: The researchers developed regression models for a number of analysis scenarios and by days of week separately. Based on the crash data, the team used an eight-week impact period for the spring time change and a four-week impact period for the fall time change. Results: The regression analysis showed that the spring time change was followed by an overall crash reduction of 18% during the eight-week period immediately after the time change, with a considerably higher crash reduction for freeway crashes (24%), rural crashes (24%), or non-intersection crashes (21%). The time change back to SDT in fall was followed by a 6% overall crash increase during the following four weeks, with considerably higher increases for freeway crashes (15%), non-intersection crashes (9%), nighttime crashes (28%), single-vehicle crashes (28%), and crashes in urban areas (12%). This study provides additional insights, including in many cases more comprehensive knowledge on how the changes to and from DST each year affect roadway traffic crashes. Practical Applications: The findings of this project further add to the current understanding on how the time changes affect public health in the form of traffic crashes. They also serve as additional evidence for public policy makers to better weigh the benefits and impacts associated with the time changes in the United States for relevant policy makings.

Injury profiles and epidemiology of single vehicle motorcycle fatalities in Tamil Nadu, India, 2009-2017

This article investigates single-vehicle motorcycle crashes and crash risk where a rider is killed or injured in Tamil Nadu, India. Hospital data linked with police-reported crash data, including demographic information, injury characteristics, and environmental and road characteristics, were analysed for all single-vehicle motorcycle crashes between January 2009 and December 2017. A total of 16,541 single-vehicle motorcycle crashes resulted in injuries (fatal, serious, and minor injury) during the period, among which 7,447 were fatal crashes. The majority of fatal crashes involved male motorcyclists aged between 26 and 64 years (5,287). Those aged 18 and 25 years (1,897) were the next most frequent group of fatally injured motorcyclists. Among the fatal injured motorcyclists, 66.6% were wearing a helmet. When reported, 33.8% of motorcyclists did not have a valid driving licence at the time of the incident. Motorcyclists injured most commonly sustained head injuries (51.3%), followed by multiple injuries (20.1%), leg injuries (3.3%), and hand injuries (3.2%). Logistic regression results suggest that specific road characteristics and environmental factors increase the probability of fatal crashes: urban roads, express highways, multiple lane roads, night time, absence of median divider, cross and T-junctions, junctions with four arms, under-aged (<18 years) riders, not holding a valid licence and non-use of helmets. Appropriate countermeasures to mitigate motorcyclist trauma are recommended.

Determinants of Influence Factors in Crashes Alongside an Intersection Using a Multinomial Logit Approach

Traffic violations are the leading cause of fatalities and injuries, and current trends indicate that this will proceed in the foreseeable future, especially alongside intersections. This study aims to examine the determinants of crash influencing factors alongside intersections using unweighted data points collected from the Michigan counties of Wayne, Genesee, and Macomb. Firstly, the random forest method identifies the significant predictor variables. Meanwhile, six predictor variables are considered: the speed limit, distance, light condition, crash location, train involvement, and weather conditions. Secondly, the multinomial logit approach is adopted to investigate the relationships among crash violation categories. Thus, the model predicts the likelihood of crash violation outcomes: angle, head-on, rear-end, rear-end right/left turn, same sideswipe, opposite direction, and single-vehicle crash. Likewise, the results confirmed that the predictor variables of the distance between 0 and 82 ft and train involvement increased the likelihood of a crash violation when driving at a speed limit of 26–50 mph. In addition, when the speed limit is between 0 and 25 mph, the odds ratios were greater than 1.0 (4.01, 3.85), indicating statistically significant positive relationships between the same sideswipe and opposite direction violations. Therefore, speeding is risky, but the safest speed is not always the slowest speed. Thus, the results provide evidence that the predictor variables are linked to various crash violations. Consequently, the findings of this study can be used to reduce the occurrence of crash violation and improve intelligent modes of transportation by transforming large datasets into knowledge and actionable intelligence considering the future roles of driving strategies.

Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data

Real-time prediction of crash risk can support traffic incident management by generating critical information for practitioners to allocate resources for responding to anticipated traffic crashes proactively. Unlike previous studies using archived traffic data covering a limited highway environment such as a segment or corridor, this study uses a statewide live traffic database from HERE to develop real-time traffic crash prediction models. This database provides crowdsourced probe vehicle data that are high-resolution real-time traffic speed for the entire freeway network (nearly 2,000 miles) in Alabama. This study aims to use machine learning models to predict crash risk on freeways according to pre-crash traffic dynamics (e.g., mean speed, speed reduction) along with static freeway attributes. Traffic speed characteristics were extracted from the HERE database for both pre-crash and crash-free traffic conditions. Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were developed and compared. Separate models were estimated for three major crash types: single-vehicle, rear-end, and sideswipe crashes. The model prediction accuracy indicated that the RF models outperform other models. Models for rear-end crashes are found to have greater accuracy than other models, which implies that rear-end crashes have a significant relationship with pre-crash traffic dynamics and are more predictable. The traffic speed factors that are ranked high in terms of feature importance are the speed variance and speed reduction prior to crashes. According to partial dependence plots, the rear-end crash risk is positively related to the speed variance and speed reductions. More results are discussed in the paper.

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.

Drivers’ ability to distinguish consecutive horizontal curves

Driver error in distinguishing preceding and upcoming horizontal curves can lead to single-vehicle fatal crashes. The 3D perspective views of different horizontal curve stimuli were used to examine a representative sample of volunteering drivers’ ability to distinguish consecutive horizontal curves. A probit model, developed using the recorded responses, revealed that differences in radius and deflection angle between the consecutive curves are more likely to influence, whereas radius and deflection angle of reference curve are less likely to influence drivers in distinguishing consecutive curves. The sensitivity analysis of the model parameters indicated a difference in radius between the consecutive curves as the most and deflection angle of reference curve as the least sensitive parameters. The estimated marginal effects are useful for evaluating the design and safety of consecutive curves from the drivers’ perspective. Finally, nomograms were developed for relevant applications.

Cellphone-distracted crashes of novice teen drivers: Understanding associations of contributing factors for crash severity levels and cellphone usage types

Objective As novice teen drivers are uniquely susceptible to the harmful effects of secondary activities on cellphones, 38 states and Washington D.C. have banned all types of cellphone usage for drivers younger than 18 years or in the learner/intermediate phase of driving. Despite the prevalence of such cellphone prohibitions, several surveillance studies have highlighted the persistent engagement of teenagers in cellphone-distracted driving, which increases the related crash risk. Most of the prior studies broadly consider cellphone usage as a general distraction instead of investigating different distraction-related tasks associated with cellphone use. This study analyzed the cellphone crashes of novice teenagers (aged 15–17 years) to discover the grouping of contributing factors by crash severity levels and cellphone usage types. Methods The current study collected five years (2015–2019) of related crash data from the Louisiana Department of Transportation and Development. A manual effort was carried out to recognize the type of cellphone tasks before collision by reading the narratives of police-investigated crash reports. Association rule mining was applied to explore the associations between numerous crash attributes in multiple circumstances without relying on any predetermined hypotheses. Results The cumulative effect of cellphone distraction and no seatbelt usage is frequently visible in confirmed injury crash scenarios. Cellphone crashes of novice teenagers at intersections are strongly associated with talking/listening rather than texting/browsing/dialing and reaching for/answering/locating. The associations among environmental factors and modes of cellphone usage significantly influence the manner of collisions. Single-vehicle crashes are associated with cellphone manipulation while driving on weekends in cloudy weather, whereas sideswipe collisions are frequent in evening hours during reaching for/answering/locating the cellphones. In relation to texting/browsing/dialing, novice teenagers operating vans/SUVs are strongly associated with traffic control violations. Conclusions The findings are expected to be beneficial for policymakers and other safety officials to develop strategic planning and implementable countermeasures when dealing with cellphone-distracted novice teenagers. The association of factors identified from the analysis exhibits real-world crash scenarios critical to strengthening driver education programs to mitigate teen driver crashes. Moreover, cellphone crashes and related casualties can be reduced by eliminating or improving one of the attributes involved in the crash patterns.

The prevalence and excess mortality risk of driving with children

Introduction: The presence of passengers can affect the driving behavior of motor-vehicle operators. Child passengers present unique motivations to drive more safely, as well as opportunities to distract drivers. Because motor-vehicle crashes are an important cause of premature childhood mortality, this study assesses whether adult drivers with child passengers are more or less likely to cause a fatal crash. Method: Data include fatal crashes involving one or two vehicles from 2007 to 2017 in the U.S. Fatality Analysis Reporting System. We apply methods developed by Levitt and Porter (2001) and Dunn and Tefft (2020) -the LPDT approach- to estimate the risk that adult drivers (21 years or older) with at least one child passenger (15 year or younger) cause a fatal crash relative to adults without child passengers. Results: Childhood crash exposure when traveling with an adult driver is low: 0.78% of vehicle miles traveled by adults included a child passenger. Nevertheless, adult drivers with child passengers were significantly more likely to cause a fatal crash than adult drivers without child passengers. The estimated risk of causing a single-vehicle crash was 6.2 times higher among the full sample of adults, 7.2 times higher among female drivers, and 5.0 times higher among drivers 25–44 years old. Conclusions: Despite their relatively low crash exposure, child passengers are associated with much greater risk of causing a fatal crash. Practical Applications: This study not only informs about the need to develop interventions to remind parents and adult drivers of the risks associated with driving children, but also reminds researchers about the enormous potential of the LPDT approach when applied to traffic safety issues.

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

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

An empirical analysis of how asleep/fatigued driving-injury severities have changed over time

Asleep/fatigued driving has proven to be a serious and persistent highway-safety problem. This study investigates aspects of this problem by studying the temporal changes in driver-injury severities in single-vehicle crashes that involve asleep/fatigued driving. To do this, random parameters logit models with unobserved heterogeneity in means and variances were estimated to compare injury-severities in asleep/fatigued crashes in Florida in 2014 and 2019. The estimated models are based on available police-reported crash data that include a wide variety of factors related to the spatial, temporal, and weather characteristics as well as vehicle characteristics, traffic information, harmful events, roadway attributes, and driver characteristics. The model estimates show that there were many statistically significant factors determining driver-injury severities resulting from asleep/fatigued driving, and that the effect of these factors on driver-injury severities has changed significantly over time, with many explanatory variables producing temporally shifting marginal effects. While asleep/fatigued driving crashes remain a serious safety concern, the empirical findings indicate (using model prediction simulations) that the resulting injury severities in crashes involving asleep/fatigued driving have declined between 2014 and 2019, likely reflecting the effectiveness of safety campaigns and ongoing improvements in vehicle safety technologies and highway safety features.

Differences of overturned and hit-fixed-object crashes on rural roads accompanied by speeding driving: Accommodating potential temporal shifts

Overturned crashes are associated with a disproportionate number of severe injuries and fatalities, while hit-fixed-object crashes are acknowledged as the most frequent single-vehicle crashes. To investigate the temporal stability and differences of contributing factors determining different injury severity levels in overturned and hit-fixed-object crashes on rural roads accompanied by speeding driving, this paper estimates two groups of correlated random parameters logit models with heterogeneity in the means (one group relating to overturned crashes and the other relating to hit-fixed-object crashes). Three injury-severity categories are determined as outcome variables: severe injury, minor injury and no injury, while multiple factors are investigated as explanatory variables including driver, vehicle, roadway, environmental, and crash characteristics. The overall temporal instability and non-transferability between overturned and hit-fixed-object crashes are captured through likelihood ratio tests. Marginal effects are adopted to further exhibit temporal variations of the explanatory variables. Despite the overall temporal instability, some variables still present relative temporal stability such as alcohol, truck, aggressive driving, vehicle age (>14 years old), and speed limit (<45 mph). This non-transferability between overturned and hit-fixed-object crashes provides insights into developing differentiated strategies targeted at mitigating and preventing different types of crashes. Besides, out-of-sample prediction is undertaken given the captured temporal instability and non-transferability of overturned and hit-fixed-object crash observations. More studies can be conducted to accommodate the spatial instability, under-reporting of severe-injury crashes, the trade-off between model predictive capability, inference capability, and selectivity bias.

A comparative study of factors associated with motorcycle crash severities under different causal scenarios

This study was carried out to examine the factors associated with motorcycle crash severity in Alabama, under different manner of crash and causal scenarios using mixed logit modeling. Three crash mechanisms were considered in this study: single-vehicle motorcycle crash with motorcyclist at fault, multi-vehicle collision between a motorcycle and another vehicle with motorcyclist being at fault, and motorcyclist not at fault in a collision between a motorcycle and another vehicle. The model estimation results showed that crashes that happened in rural areas were more likely to be severe, irrespective of the causal unit or manner of collision. The results also show that fatigue among motorcyclists was associated with severe injury, whereas driver fatigue was linked to no injury outcome. Further, it was found that risky behaviors such as speeding, driving/riding under the influence of alcohol or drugs, driving/riding with invalid license were significantly associated with severe injury outcome. Developing the injury-severity models based on the segmented crash data has helped to reveal some similarities and differences in crash outcomes based on the crash mechanism and the at-fault road user. It is expected that these findings would provide a data-driven evidence to improve motorcycle safety in the state.

Driving offences and risk of subsequent crash in novice drivers: the DRIVE cohort study 12-year follow-up

BackgroundPenalties are a key component to improve road user safety, but previous studies suggested that they might not be successful in reducing crashing in offending drivers. However, these studies were...

Characterizing the differences of injury severity between single-vehicle and multi-vehicle crashes in China

It is of paramount importance for mitigating road crash losses to characterize the relationship between crash injury severities and contributing factors. Existing studies have revealed mechanism differences of single-vehicle (SV) and multi-vehicle (MV) crashes. This study positions itself at exploring the differences from spatiotemporal, road-environment, driver-vehicle, and collision characteristics. A model comparison as well as the elasticities for the optimal model (partial proportional odds model) is implemented based on 18,083 SV crashes and 22,162 MV crashes in China. The results evidenced the great differences that time, road, speed, lighting, and weather are found to have a positive correlation with only SV crash injury severity, yet negatively related with only MV crash injury severity. Area, location, and angle are significant only for SV crashes, while day, interference, and wind are significant only for MV crashes. The findings revealed that gender, age, collision, location, and time are more influencing factors in SV crashes, while collision, age, gender, vehicle, and wind have more contributions to MV crashes. The findings could provide an insightful reference for prioritizing effective countermeasures to mitigate traffic crash losses.

A systematic review and meta-analysis of the impact of curbs on crash outcomes

Introduction Road traffic crashes involving vertical curbs are commonly reported to occur on highways and expressways in India. We found a gap in terms of systematically assessing the evidence of the impact of curbs on road safety outcomes in the real world. Method We conducted a systematic review and meta-analysis of the impact of curbs on the risk of road traffic injuries. We used keywords in a database of records prepared by an earlier evidence gap map (EGM). The EGM used a comprehensive search strategy including 6 academic database, 17 organizational websites, hand searching, contacting experts and back referencing. Results We found 4 studies that evaluated impact of a curbed median or a curbed shoulder. We found that the presence of a curb on a median increases the risk for all crashes, all single-vehicle crashes, all median-related crashes and median-related injury crashes. The data also indicate that the severity of accidents reduces for curbs on median while it increases for curbs on shoulder, though the latter effect is not statistically significant. All the epidemiological studies were conducted on rural highways and did not report effects for different traffic speeds or vehicle types. However, our review of crash tests and simulation studies indicates that the impact of a curb design may be highly sensitive to speed and vehicle types. Conclusions The safety impacts of a curb depend on the context of the road. In an urban road, a curb should ensure safety of pedestrians from an errant vehicle. On high-speed rural roads, curbs should be avoided and treatments should facilitate safe departure of the vehicle from the roadway.