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 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.

Examining Injury Severity of Not-At-Fault Drivers in Two-Vehicle Crashes

2017 ◽  
Vol 2659 (1) ◽  
pp. 164-173 ◽  
Author(s):  
Praveena Penmetsa ◽  
Srinivas S. Pulugurtha ◽  
Venkata R. Duddu
Keyword(s):  
Physical Condition ◽  
Injury Severity ◽  
Probit Model ◽  
Ordered Probit ◽  
Severe Injury ◽  
Ordered Probit Model ◽  
Vehicle Crashes ◽  
Severe Injuries ◽  
Severity Of Injury ◽  
Traffic Rule

The focus of this paper is to examine the injury severity of not-at-fault drivers in two-vehicle crashes. North Carolina crash data collected from 2009 to 2013 were used for the analysis. Ordered probit model was initially chosen because of the ordinal nature of the dependent variable (injury severity of the driver not at fault). However, the data failed to obey the proportional odds assumption accompanied with the ordered probit model. Therefore, a partial proportional model was fitted for two-vehicle crashes. Compared with the physical condition of at-fault drivers, the physical condition of not-at-fault drivers had a greater effect on the severity of injury to the not-at-fault drivers. Exceeding the speed limit, aggressive or reckless driving, and going the wrong way are the three traffic rule violations of at-fault drivers that are more likely to result in severe injuries to not-at-fault drivers than disregarding traffic signs, signals, and markings. Similarly, a crash involving an at-fault driver with violations of two and three traffic rules is 1.68 and 2.86 times likely to result in severe injuries to not-at-fault drivers compared with a crash involving an at-fault driver with only one traffic rule violation. Motorcyclists are observed to be at highest risk with the odds of severe injury to motorcyclists who are not at fault. Crashes with female at-fault drivers are less likely to result in severe injury to the not-at-fault drivers. Female drivers are also more likely to be severely injured when they are not at fault.

Applying the Ordered Probit Model to Injury Severity in Truck-Passenger Car Rear-End Collisions

1998 ◽  
Vol 1635 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Chandler S. Duncan ◽  
Asad J. Khattak ◽  
Forrest M. Council
Keyword(s):  
High Speed ◽  
Injury Severity ◽  
Probit Model ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Passenger Cars ◽  
Passenger Car ◽  
Vehicle Occupant ◽  
Occupant Injury ◽  
The Impact

Collisions between heavy trucks and passenger cars are a major concern because of the severity of injuries. This research has two objectives. One is to examine the impact of various factors on injuries to passenger car occupants involved in such collisions. Due to the complex interaction of factors influencing injury levels in truck-car collisions, the ordered probit model is used to identify specific variables significantly influencing levels of injury in two-vehicle rear-end involvements on divided roadways. Another objective is to demonstrate the use of the ordered probit in this complex highway safety problem. A set of vehicle, occupant, roadway, and environmental factors expected to influence injury severity was developed. Given two-vehicle passenger car-truck rear-end collisions, the variables that increase passenger vehicle occupant injury severity include darkness; high speed differentials; high speed limits; grades, especially when they are wet; being in a car struck to the rear (as opposed to being in a car striking a truck to the rear); driving while drunk; and being female. The interaction effects of cars being struck to the rear with high speed differentials and car rollovers were significant. Variables decreasing severity include snowy or icy roads, congested roads, being in a station wagon struck to the rear (as opposed to a sedan), and using a child restraint. With injuries ordered in five classes from no injury to fatalities, the marginal effects of each factor on the likelihood of each injury class are reported.

scholarly journals Estimation of nested and zero-inflated ordered probit models

2021 ◽  
Vol 21 (1) ◽  
pp. 3-38
Author(s):  
David Dale ◽  
Andrei Sirchenko
Keyword(s):  
Goodness Of Fit ◽  
Model Comparison ◽  
Probit Model ◽  
Information Criteria ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Finite Sample ◽  
Federal Funds Rate ◽  
Probit Models ◽  
Ordered Probit Models

We introduce three new commands—nop, ziop2, and ziop3—for the estimation of a three-part nested ordered probit model, the two-part zero-inflated ordered probit models of Harris and Zhao (2007, Journal of Econometrics 141: 1073–1099) and Brooks, Harris, and Spencer (2012, Economics Letters 117: 683–686), and a three-part zero-inflated ordered probit model of Sirchenko (2020, Studies in Nonlinear Dynamics and Econometrics 24: 1) for ordinal outcomes, with both exogenous and endogenous switching. The three-part models allow the probabilities of positive, neutral (zero), and negative outcomes to be generated by distinct processes. The zero-inflated models address a preponderance of zeros and allow them to emerge in different latent regimes. We provide postestimation commands to compute probabilistic predictions and various measures of their accuracy, to assess the goodness of fit, and to perform model comparison using the Vuong test (Vuong, 1989, Econometrica 57: 307–333) with the corrections based on the Akaike and Schwarz information criteria. We investigate the finite-sample performance of the maximum likelihood estimators by Monte Carlo simulations, discuss the relations among the models, and illustrate the new commands with an empirical application to the U.S. federal funds rate target.

scholarly journals Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

2019 ◽  
Vol 16 (14) ◽  
pp. 2632 ◽  
Author(s):  
Chen ◽  
Song ◽  
Ma
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Seat Belt ◽  
Probit Model ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Random Parameters ◽  
Factors Affecting ◽  
Bivariate Ordered Probit ◽  
Bivariate Ordered Probit Model

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

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