Injury Severity of Pedestrians at Mid-blocks: A Random Parameter Ordered Probit Approach

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.

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

Journal of Advanced Transportation ◽

10.1155/2020/8870497 ◽

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.

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

European Transport Research Review ◽

10.1186/s12544-021-00524-z ◽

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.

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

Journal of the Transportation Research Forum ◽

10.5399/osu/jtrf.54.1.4273 ◽

2015 ◽

Cited By ~ 1

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.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211009882 ◽

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.

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