The temporal stability of factors affecting driver injury severity in run-off-road crashes: A random parameters ordered probit model with heterogeneity in the means approach

Yu, Miao; Zheng, Changjiang; Ma, Changxi; Shen, Jinxing

doi:10.1016/j.aap.2020.105677

Cited by 52 publications

(18 citation statements)

References 20 publications

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“…To account for the effects of unobserved heterogeneity, the coefficients

are allowed to vary across observations for selected independent variables. Past research has shown that this approach, known as random parameters ordered probit (RPOP) modelling, often significantly improves the explanatory power of the framework ( Anastasopoulos and Mannering, 2009 ; Mannering et al, 2016 ; Seraneeprakarn et al, 2017 ; Yu et al, 2020 ), when compared to the traditional fixed parameters ordered probit (FPOP). To optimize the layers of unobserved heterogeneity captured by the modelling framework, allowances are also made for heterogeneity in the means of random parameters; hence, the complete modelling approach used for the statistical analysis is referred to as the Random Parameters Ordered Probit with Heterogeneity in the Means of random parameters (RPOPHM).…”

Section: Methodsmentioning

confidence: 99%

“…To optimize the layers of unobserved heterogeneity captured by the modelling framework, allowances are also made for heterogeneity in the means of random parameters; hence, the complete modelling approach used for the statistical analysis is referred to as the Random Parameters Ordered Probit with Heterogeneity in the Means of random parameters (RPOPHM). This approach is considered a more comprehensive way of capturing unobserved heterogeneity, as random parameters are allowed to vary by explanatory variables ( Seraneeprakarn et al, 2017 ; Yu et al, 2020 ). The revised framework can be written as follows:

where

is a vector of estimable parameters that may vary across observations, n ,

is the vector of mean parameter estimates across the dataset,

is a vector of explanatory variables from observation n , that influence the mean of

is a vector of estimable parameters and

is a vector of random distributed terms.…”

Section: Methodsmentioning

confidence: 99%

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Trips for outdoor exercise at different stages of the COVID-19 pandemic in Scotland

Semple

Fountas

Fonzone

2021

Journal of Transport & Health

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Introduction The COVID-19 pandemic has had exceptional effects on travel behaviour in the UK. This paper focuses specifically on the outdoor exercise trips of Scottish residents at several distinct points of the COVID-19 pandemic. Given the negative health consequences of limited exercise, this study aims to determine the sociodemographic and behavioural factors affecting frequency of outdoor exercise trips. Methods Using recent public survey data (n=6000), random parameters ordered probit models (with allowances for heterogeneity in the means of random parameters) are estimated for three points during the pandemic: the most stringent lockdown, modest restriction easing and further easing of restrictions. Results The survey data show frequent outdoor exercise in the early stages of the pandemic, with ∼46% making six or more weekly trips during lockdown, reducing to ∼39% during the first phase of restriction easing, and further to ∼34% during the following phase of easing. The model estimations show that common factors, dominated by socioeconomic and demographic variables, influenced the frequency of outdoor exercise trips across most survey groups. The modelling framework also allowed insights into the impact of unobserved characteristics within several independent variables; for example, the lockdown exercise trip rates of those with a health problem or disability, and those over 65, were both found to be dependent on personal vehicle access. Conclusions The findings suggest that those with a health problem or disability, those who live in households’ where the main income earner is employed in a semi-skilled/unskilled manual occupation or is unemployed and ethnic minority groups (i.e., any mixed, Asian, or Black background) were significantly more likely to complete no weekly outdoor exercise trips throughout the pandemic. As a result, we suggest that these groups are at higher risk of the negative health consequences associated with limited physical activity. Policy implications are discussed in terms of mitigating this effect, as well as reducing transport inequity related to vehicle accessibility.

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“…To account for the effects of unobserved heterogeneity, the coefficients

Section: Methodsmentioning

confidence: 99%

where

is a vector of estimable parameters that may vary across observations, n ,

is the vector of mean parameter estimates across the dataset,

is a vector of explanatory variables from observation n , that influence the mean of

is a vector of estimable parameters and

is a vector of random distributed terms.…”

Section: Methodsmentioning

confidence: 99%

Trips for outdoor exercise at different stages of the COVID-19 pandemic in Scotland

Semple

Fountas

Fonzone

2021

Journal of Transport & Health

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“…To accommodate the discrete nature of crash severity (no injury, slight injury, serious injury, and fatality), various regression approaches-random parameters logit (RP-logit) model [38,39], random parameters probit model [40], random intercept logit model [41], latent class logit model [10], and finite mixture random parameters model [16,42]-have been widely recommended due to their high flexibility [43][44][45]. Alternatively, random parameters ordered logit model [46] and random parameters ordered probit model [47] were applied to handle the intuitive ordering of crash severity. For example, Wu et al [8] established the RP-logit model to analyze the risk factors of single-and multi-vehicle crash severity on rural highways.…”

Section: Statistical Techniques For Unobserved Heterogeneity and Spatial Correlationmentioning

confidence: 99%

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

et al. 2021

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Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.

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“…Since investigating factors contributing to crash severity provides important information for traffic safety agencies, related studies have been performed for different crash types, such as work-zone related crashes (14), run-off-road crashes (15), motorcyclists' injuries (16,17), bicycle-vehicle crashes (18), single-vehicle crashes (19), and pedestrian collisions (20). Typically, as a result of reported discrete injury-severity levels in the crash data, the discrete response approaches (including both the ordered and non-ordered response model) have always been used by researchers (14)(15)(16)(17)(18)(19)(20)(21)(22)(23). However, limited research efforts employed discrete response approaches to analyze the WWD crash severity.…”

mentioning

confidence: 99%

“…Among these approaches, one of the commonly used methods is the random parameters logit model, which allows certain variables to vary across the observation (25). Moreover, to further consider the issue of unobserved heterogeneity, recent studies have found that the random parameters logit model that accounts for the potential heterogeneity in means and variances of random parameters produced a statistically superior fit to the crash data (15)(16)(17)(18)(19)(20)23). To this end, this study intends to investigate the effects of the selected explanatory on the driver injury severity in WWD crashes while taking unobserved heterogeneity into account.…”

mentioning

confidence: 99%

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

Shen

2021

Transportation Research Record: Journal of the Transportation R

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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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The temporal stability of factors affecting driver injury severity in run-off-road crashes: A random parameters ordered probit model with heterogeneity in the means approach

Cited by 52 publications

References 20 publications

Trips for outdoor exercise at different stages of the COVID-19 pandemic in Scotland

Trips for outdoor exercise at different stages of the COVID-19 pandemic in Scotland

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

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

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