scholarly journals Exploring Driver Injury Severity in Single-Vehicle Crashes under Foggy Weather and Clear Weather

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fulu Wei ◽  
Zhenggan Cai ◽  
Pan Liu ◽  
Yongqing Guo ◽  
Xin Li ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Injury Severity ◽  
Mixed Logit ◽  
Weather Conditions ◽  
Crash Severity ◽  
Young Drivers ◽  
Self Employment ◽  
Road Surfaces ◽  
Single Vehicle ◽  
Clear Weather

The purpose of this study is to investigate and compare the significant influencing factors of driver injury severity in single-vehicle (SV) crashes under foggy and clear weather conditions. Based on data for SV crashes in Shandong Province, China, the mixed logit model (MLM) was employed to interpret driver injury severity for SV crashes in clear and foggy weather. The results showed that there are significant differences in the influencing factors of the severity of SV crashes in foggy and clear weather. Specifically, 15 factors are significantly associated with the severity of SV crashes in clear weather, and 18 factors are significantly associated with the severity of SV crashes in foggy weather. In addition, young drivers (age < 30), non-dry road surfaces, and signal control significantly influence the severity of foggy weather crashes but not clear weather crashes. Self-employment and weekends have significant effects on the severity of crashes only in clear weather. Interestingly, drivers whose occupation is farming showed opposite trends in the effect of crash severity in foggy and clear weather. Based on the findings of this research, some potential countermeasures can be adopted to reduce crash severity in foggy and clear weather.

Analysis of Severity of Young Driver Crashes: Sequential Binary Logistic Regression Modeling

2002 ◽  
Vol 1784 (1) ◽  
pp. 108-114 ◽  
Author(s):  
Sunanda Dissanayake ◽  
John Lu
Keyword(s):  
Logistic Regression ◽  
Injury Severity ◽  
Selection Process ◽  
Model Development ◽  
Weather Condition ◽  
Binary Logistic Regression ◽  
The United States ◽  
Crash Severity ◽  
Young Drivers ◽  
Single Vehicle

Young drivers have the highest fatality involvement rates of any driver age group within the United States driving population. They also experience a higher percentage of single-vehicle crashes compared with others. When looking at the methods of improving this alarming death rate of young drivers, it is important to identify the determinants of higher crash and injury severity. With that intention, the study developed, using the Florida Traffic Crash Database, a set of sequential binary logistic regression models to predict the crash severity outcome of single-vehicle fixed-object crashes involving young drivers. Models were organized from the lowest severity level to the highest and vice versa to examine the reliability of the selection process, but it was found that there was no considerable impact based on this selection. The developed models were validated and the accuracy was tested by using crash data that were not utilized in the model development, and the results were found to be satisfactory. Factors influential in making a crash severity difference to young drivers were then identified through the models. Factors such as influence of alcohol or drugs, ejection in the crash, point of impact, rural crash locations, existence of curve or grade at the crash location, and speed of the vehicle significantly increased the probability of having a more severe crash. Restraint device usage and being a male clearly reduced the tendency of high severity, and some other variables, such as weather condition, residence location, and physical condition, were not important at all.

Modeling head-on crash severity on NCDOT freeways: a mixed logit model approach

2019 ◽  
Vol 46 (4) ◽  
pp. 322-328 ◽  
Author(s):  
Pengfei Liu ◽  
Wei (David) Fan
Keyword(s):  
Logit Model ◽  
Injury Severity ◽  
Safety Information ◽  
Mixed Logit ◽  
Weather Condition ◽  
Mixed Logit Model ◽  
Young Drivers ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
Model Approach

This study employs a mixed logit model approach to evaluate contributing factors that significantly affect the severity of head-on crashes. The head-on crash data are collected from Highway Safety Information System (HSIS) from 2005 to 2013 in North Carolina. The effects that vehicle, driver, roadway, and environmental characteristics have on the injury severity of head-on crashes are examined. The results of this research demonstrate that adverse weather, young drivers, rural roadways, and pickups are found to be better modeled as random-parameters at specific injury severity levels, while others should remain fixed. Also, the model results indicate that driving under the influence of alcohol or drugs, grade or curve roadway configuration, old drivers, high speed limit, motorcycles will increase the injury severity of head-on crashes. Adverse weather condition, two-way divided road, traffic control, young drivers, and pickups will decrease the injury severity of head-on crashes.

scholarly journals Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel

2020 ◽  
Vol 17 (9) ◽  
pp. 3155 ◽  
Author(s):  
Shengdi Chen ◽  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu
Keyword(s):  
Environmental Factor ◽  
High Speed ◽  
Injury Severity ◽  
Control Measures ◽  
Crash Severity ◽  
Contributing Factors ◽  
Safety Control ◽  
Late Night ◽  
Single Vehicle ◽  
The Impact

The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00–06:59) and afternoon rushing hours (16:30–18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures.

scholarly journals The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Changxi Ma ◽  
Wei Hao ◽  
Wang Xiang ◽  
Wei Yan
Keyword(s):  
Open Space ◽  
Injury Severity ◽  
Mixed Logit ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Driving Behaviors ◽  
Grade Crossings ◽  
High Level ◽  
The Impact

The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

Driver-injury severity in single-vehicle crashes in California: A mixed logit analysis of heterogeneity due to age and gender

2013 ◽  
Vol 50 ◽  
pp. 1073-1081 ◽  
Author(s):  
Joon-Ki Kim ◽  
Gudmundur F. Ulfarsson ◽  
Sungyop Kim ◽  
Venkataraman N. Shankar
Keyword(s):  
Injury Severity ◽  
Mixed Logit ◽  
Logit Analysis ◽  
Vehicle Crashes ◽  
Age And Gender ◽  
Single Vehicle ◽  
And Gender

Analysis of Single-Vehicle Roadway Departure Crashes on Rural Curved Segments Accounting for Unobserved Heterogeneity

2020 ◽  
Vol 2674 (10) ◽  
pp. 146-157
Author(s):  
Mouyid Islam ◽  
Anurag Pande
Keyword(s):  
Environmental Conditions ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Safety Information ◽  
Mixed Logit ◽  
Highway Safety ◽  
Improvement Program ◽  
Cloudy Weather ◽  
Adverse Weather ◽  
Single Vehicle

Roadway departure crashes are one of the core emphasis areas in Strategic Highway Safety Plans (SHSP). These crashes, especially on rural roads, lead to a disproportionately higher number of fatalities and serious injuries. The focus of this study is to identify and quantify the factors affecting injury-severity outcomes for single-vehicle roadway departure (SV-RwD) crashes on rural curved segments in Minnesota. The crash data are extracted from the Highway Safety Information System (HSIS) from 2010 to 2014. This study applied a mixed logit with heterogeneity in means and variances approach to model driver-injury severity. The approach accounts for possible unobserved heterogeneity in the data resulting from driver, roadway, traffic, environmental conditions, or any combination of these attributes. This analysis adds value to the growing body of literature because it uncovers some unobserved heterogeneity in the form the attributes specific to driver-injury severities in contrast to the standard mixed logit approach. The model results indicate that there is a complex interaction of driver characteristics and actions (male drivers, aged below 30 years of age, and unsafe speed), roadway and traffic characteristics (two-lane undivided road, county roadways, and low traffic volume), environmental conditions (adverse weather, cloudy weather, dark conditions, and dry surface conditions), and vehicle characteristics (vehicle type—sport utility vehicle involved in rollover crashes). The results also provide some evidence of the effectiveness of a highway curve safety improvement program implemented in one of the Minnesota Department of Transportation (DOT) districts.

Examining Prominent Causes of Traffic Injury Severity in Louisiana with Multinomial Logistic Models

2020 ◽  
pp. 036119812096478
Author(s):  
Onyumbe Enumbe B. Lukongo
Keyword(s):  
Traffic Safety ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Logistic Models ◽  
Crash Severity ◽  
Fatal Accidents ◽  
Vehicle Accidents ◽  
The Road ◽  
Young Drivers ◽  
Safety Research

Accidents rank third among the top 10 leading causes of death in Louisiana, claiming more than 2,000 lives out of a total of almost 33,000 deaths. Drivers’ characteristics (age and gender), the geometry of the roadways, driving on the major roadways, the day of the week, and the wet or dry condition or the road have been associated with crash severity. This study applies unordered multinomial logistic models to investigate causes leading to crash severity in Louisiana. Several models were estimated and the best results were retained for presentation and discussion. Consistent with previous research, findings suggest that drivers’ gender and age matter for traffic safety. Individually, male and older drivers are too risky. Major roads, weekdays, dry surfaces, and road geometry increase the risk of fatal accidents. Male drivers are prone to severe and fatal accidents while old drivers are vulnerable to all types of accidents. Young drivers and female drivers feature among cases of injury and moderate accidents. Evidence suggests that crash severity is not ethnicity specific, contrary to some studies. This study is relevant because it builds a new dataset for safety research, identifies risk factors, and informs the aim of public safety policy to reduce loss of life, injuries, and costs resulting from motor vehicle accidents.

scholarly journals Influence of Type of Traffic Control on Injury Severity in Bicycle–Motor Vehicle Crashes at Intersections

2018 ◽  
Vol 2672 (38) ◽  
pp. 199-209 ◽  
Author(s):  
Rabbani Rash-ha Wahi ◽  
Narelle Haworth ◽  
Ashim Kumar Debnath ◽  
Mark King
Keyword(s):  
Traffic Control ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Mixed Logit ◽  
Traffic Signals ◽  
Signalized Intersections ◽  
Mitigation Strategies ◽  
Control Measures ◽  
Mixed Logit Models ◽  
Road Surfaces

Many studies have identified factors that contribute to bicycle–motor vehicle (BMV) crashes, but little is known about determinants of cyclist injury severity under different traffic control measures at intersections. Preliminary analyses of 5,388 police-reported BMV crashes from 2002 to 2014 from Queensland, Australia revealed that cyclist injury severity differed according to whether the intersection had a Stop/Give-way sign, traffic signals or no traffic control. Therefore, separate mixed logit models of cyclist injury severity (fatal/hospitalized, medically treated, and minor injury) were estimated. Despite similar distributions of injury severity across the three types of traffic control, more factors were identified as influencing cyclist injury severity at Stop/Give-way controlled intersections than at signalized intersections or intersections with no traffic control. Increased injury severity for riders aged 40–49 and 60+ and those not wearing helmets were the only consistent findings across all traffic control types, although the effect of not wearing helmets was smaller at uncontrolled intersections. Cyclists who were judged to be at fault were more severely injured at Stop/Give-way and signalized intersections. Speed zone influenced injury severity only at Stop/Give-way signs and appears to reflect differences in intersection design, rather than speed limits per se. While most BMV crashes occurred on dry road surfaces, wet road surfaces were associated with an increased cyclist injury severity at Stop/Give-way intersections. The results of this study will assist transport and enforcement agencies in developing appropriate mitigation strategies to improve the safety of cyclists at intersections.

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