scholarly journals Analysis of the impacts of risk factors on teenage and older driver injury severity using random-parameter ordered probit

2020 ◽  
Vol 47 (11) ◽  
pp. 1249-1257 ◽  
Author(s):  
Sina Darban Khales ◽  
Mehmet Metin Kunt ◽  
Branislav Dimitrijevic
Keyword(s):  
Injury Severity ◽  
Weather Condition ◽  
Random Parameter ◽  
Older Drivers ◽  
Ordered Probit ◽  
Driver Model ◽  
Age Group ◽  
Contributing Factors ◽  
Lighting Condition ◽  
Older Driver

The study analyzed injury severity of teenage and older drivers using 2015–2016 crash data from New Mexico. The fitness of the random-parameter ordered probit models developed for each age group was tested using likelihood ratio, comparing them to a unified model that combines both age groups, as well as comparing the random-parameter to fixed-parameter ordered probit for each age group. In both cases separate random-parameter ordered probit provided better results. It was found that vehicle type and age, lighting condition, alcohol or drug use, speeding, and seatbelt use were significant both for the teenage and older driver injury severity. The weather condition and gender were significant only in the teenage driver model, while driver inattention was significant for older drivers. The impacts of crash factors on injury severity was analyzed using marginal effects. The results indicate notable differences in the effects of contributing factors on driver injury severity between teenage and older drivers, including the sensitivity to changes in the mutual predictor parameter values.

Multi-Vehicle Crashes Involving Large Trucks: A Random Parameter Discrete Outcome Modeling Approach

2015 ◽  
Author(s):  
Mouyid Islam
Keyword(s):  
Injury Severity ◽  
Economic Impacts ◽  
Mixed Logit ◽  
Random Parameter ◽  
Ordered Probit ◽  
Vehicle Crashes ◽  
Contributing Factors ◽  
Modeling Approach ◽  
Complex Interactions ◽  
Injury Severities

A growing concern on large-truck crashes increased over the years due to the potential economic impacts and level of injury severity. This study aims to analyze the injury severities of multi-vehicle large-trucks crashes on national highways. To capture and understand the complexities of contributing factors, two random parameter discrete outcome models – random parameter ordered probit and mixed logit – were estimated to predict the likelihood of five injury severity outcomes: fatal, incapacitating, non-incapacitating, possible injury, and no-injury. Estimation findings indicate that the level of injury severity is highly influenced by a number of complex interactions of factors, namely, human, vehicular, road-environmental, and crash dynamics that can vary across the observations.

scholarly journals Contributing Factors to Older-Driver Injury Severity in Rural and Urban Areas

2012 ◽  
Vol 49 (1) ◽  
Author(s):  
Loshaka Perera ◽  
Sunanda Dissanayake
Keyword(s):  
Urban Areas ◽  
Injury Severity ◽  
Older Drivers ◽  
Contributing Factors ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Rural And Urban Areas ◽  
Older Driver ◽  
Rural And Urban ◽  
Road Type

Older drivers tend to be involved in more severe crashes compared to middle-aged drivers, and U.S. Census Population statistics indicate that the older-driver population is rapidly increasing. Therefore, an improvement in older-driver safety is both important and necessary. In this analysis, a statistical modeling technique was used to identify factors contributing to older-driver injury severity. Two separate models were developed for rural and urban locations, which incorporated several potential explanatory variables. Speed, gender, presence of passengers, road type and street-lighting conditions were found to be important factors affecting injury severity of older drivers on both rural and urban roads.

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

2021 ◽  
pp. 036119812110098
Author(s):  
Miao Yu ◽  
Jinxing Shen ◽  
Changxi Ma
Keyword(s):  
High Speed ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Research Effort ◽  
Random Parameter ◽  
Policy Implications ◽  
Contributing Factors ◽  
Severe Injuries ◽  
Data Set ◽  
Severity Levels

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

2021 ◽  
pp. 036119812110312
Author(s):  
Denis Elia Monyo ◽  
Henrick J. Haule ◽  
Angela E. Kitali ◽  
Thobias Sando
Keyword(s):  
Latent Class ◽  
Time Of Day ◽  
Older Drivers ◽  
Contributing Factors ◽  
Lighting Condition ◽  
Crash Data ◽  
Traffic Characteristics ◽  
Road Users ◽  
Traffic Conflicts ◽  
Penalized Logistic Regression

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

scholarly journals Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Shikun Xie ◽  
Xiaofeng Ji ◽  
Wenchen Yang ◽  
Rui Fang ◽  
Jingjing Hao
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Random Parameter ◽  
Probit Model ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Crash Severity ◽  
Critical Factors ◽  
Traffic Crashes ◽  
Rural Roads

Understanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, and few studies have investigated the issue of heterogeneity on two-lane rural roads. The purpose of this study is to uncover the risk factors influencing crash severity on two-lane rural roads in China. A sample of 1490 traffic crashes occurring on two-lane rural roads between 2012 and 2017 was collected from the Mouding County Highway Bureau in Yunnan, China. A random-parameter ordered probit model was estimated using these data to capture underlying unobserved characteristics in personal traits, vehicle attributes, roadway conditions, environmental factors, and crash attribute. To better understand the effect of critical factors on crash severity outcome probability, an elasticity analysis was then introduced. The results show that six factors such as driver’s attribution, illegal driving behaviour, access segment, day of week, vehicle type, and crash form have a significant impact on the injury severity, and the impacts of driving behaviours, access segment, and vehicle-fixed object crashes had significant variation across observations. Besides, the correlations between critical factors and the probability of serious injury sustained in traffic crashes are identified and discussed. The local driver indicator has more positive impact on the crash severity than nonlocal driver, and nonaccess segment appears a higher probability of serious or vicious collisions. It is worth mentioning that motorcycle-involved crashes do show an obvious correlation with crash injury severity. As for crash forms, vehicle-vehicle crashes are more likely to lead to severe crash injury. Besides, high-risk driving behaviour (e.g., fatigue driving, speeding, and converse driving), weekends, and holidays are found to have significant contribution to increasing the probability of traffic crash injuries and fatalities on two-lane rural roads.

Experimental Intercomparisons of Younger and Older Driver Perceptions

1993 ◽  
Vol 36 (3) ◽  
pp. 239-253 ◽  
Author(s):  
Thomas M. Nelson ◽  
Bruce Evelyn ◽  
Robert Taylor
Keyword(s):  
Peer Group ◽  
Older Drivers ◽  
Age Group ◽  
Safety Hazards ◽  
Safe Driving ◽  
Older Driver ◽  
Driving Behaviors ◽  
Age Related ◽  
Positive Impression ◽  
Education Courses

Drivers over sixty-five years of age and drivers under twenty-one years of age have the highest relative frequency of crashes resulting in injury and death. Attitudes of these two groups were investigated using questionnaires. One hundred twenty-seven (127) younger and one hundred eight (108) older drivers who had voluntarily registered in driving education courses satisfactorily completed questionnaires about attitudes and behaviors pertinent to safe driving. Half of each sample rated the average driver in their age group and the average driver in the opposite age group as regarded thirty-three attitudes promoting safe driving, twenty courteous safe driving behaviors and eleven discourteous, unsafe driving behaviors. Data shows that younger drivers viewed older drivers as overly cautious, too slow to act and apt to cause accidents, and rated their peers as overly aggressive and discourteous. Older drivers characterized younger drivers as deficient in courtesy and safe driving practices, and they rated their peers as cautious, courteous, and aware of age-related limitations. The findings indicate that each group was aware that safety hazards are created by drivers in their age group. It also shows that both groups had a positive impression of some driving practices of their age group, and that the other group was depreciated. The outcome confirms and expands upon conclusions derived from less formal studies about how drivers perceive other roadway users. It also specifies the extent to which particular attitudes and driving practices are attributed to the peer group and to the opposite age group.

scholarly journals Exploring injury severity of pedestrian-vehicle crashes at intersections: unbalanced panel mixed ordered probit model

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Željko Šarić ◽  
Xuecai Xu ◽  
Daiquan Xiao ◽  
Joso Vrkljan
Keyword(s):  
Goodness Of Fit ◽  
Injury Severity ◽  
Random Parameter ◽  
Probit Model ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Vehicle Crashes ◽  
Proposed Model ◽  
Unobserved Effects ◽  
The Impact

AbstractAlthough the pedestrian deaths have been declining in recent years, the pedestrian-vehicle death rate in Croatia is still pretty high. This study intended to explore the injury severity of pedestrian-vehicle crashes with panel mixed ordered probit model and identify the influencing factors at intersections. To achieve this objective, the data were collected from Ministry of the Interior, Republic of Croatia from 2015 to 2018. Compared to the equivalent random-effects and random parameter ordered probit models, the proposed model showed better performance on goodness-of-fit, while capturing the impact of exogenous variables to vary among the intersections, as well as accommodating the heterogeneity issue due to unobserved effects. Results revealed that the proposed model can be considered as an alternative to deal with the heterogeneity issue and to decide the factor determinants. The results may provide beneficial insight for reducing the injury severity of pedestrian-vehicle crashes.

scholarly journals A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

2020 ◽  
Vol 17 (2) ◽  
pp. 395 ◽  
Author(s):  
Xiaojun Shao ◽  
Xiaoxiang Ma ◽  
Feng Chen ◽  
Mingtao Song ◽  
Xiaodong Pan ◽  
...  
Keyword(s):  
Injury Severity ◽  
Probit Analysis ◽  
Ordered Probit ◽  
Ratio Test ◽  
Random Parameters ◽  
Contributing Factors ◽  
Significant Difference ◽  
Car Crashes ◽  
The Difference ◽  
The Impact

Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions.

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