Investigation of factors influencing motorcyclist injury severity using random parameters logit model with heterogeneity in means and variances

Ijaz, Muhammad; Liu, Lan; Usman, Sheikh Muhammad; Zahid, Muhammad; Jamal, Arshad

doi:10.1080/13588265.2021.1959153

Cited by 28 publications

(16 citation statements)

References 45 publications

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“…Therefore, an advanced bus safety prediction model is expected to be developed in the future by considering the heterogeneity of spatial and temporal aspects according to (routes and year) and factors affecting the severity of bus accidents. In addition, an even higher performance traffic accident prediction model is expected to be developed as this study does not consider the additional analysis of the driver's traffic behavior [30] and speed.…”

Section: Discussionmentioning

confidence: 99%

“…However, it has been mainly used to analyze the probability and severity of car or truck accidents, and only some existing studies have focused on bus accidents. In the study of [29], the multivariate random-parameters Tobit model was applied to analyze the accident rate by road injury severity, and in the study of Ijaz et al [30], factors that affect the accident severity of motorcyclists were derived by considering variables with heterogeneity. Existing studies have used the random parameter methodology to argue that traffic accidents are mainly caused by drivers' traffic behavior, such as speeding and changing lanes, and these factors have heterogeneity, so they show various influences according to road sections or temporal characteristics [20,22,31].…”

Section: Literature Reviewmentioning

confidence: 99%

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Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Kim,

Hong

2023

Sustainability

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As intercity buses are a mode that moves large-scale occupancy between regions, it accounts for the mode share-means for mid- to long-distance movement in South Korea. However, the study of intercity bus safety needs to be more extensive, and safety policies are carried out based on traditional probability models without considering the data characteristics of bus accidents. Therefore, in this study, the Random Parameter Ordered Logit model was applied to derive fixed parameter factors that have the same effect on the severity of intercity bus accidents and Random Parameters that consider the heterogeneity of unique attributes by accident. It also analyzed the marginal effect of intercity bus accident severity. As a result of this study, the influencing factors that reflect heterogeneity with random parameters were driver’s condition: drowsiness, vehicle size: medium, crash type: vehicle–pedestrian accident, road condition: wet pavement, and log form of AADT. The random parameter ordered logit model was traditionally found to be more suitable than the ordinal logit model, which only reflects fixed factors and more reliable predictions considering the heterogeneity of accident characteristics for each observation.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Kim,

Hong

2023

Sustainability

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“…The aforementioned random parameters approach (the basic random parameters model) assumes that the distributional mean for the normal distribution of a random parameter is the same across observations and that all of the random parameters are independent, which is not true in many cases due to the complex interactions of both observed and unobserved variables. A more flexible way to improve the basic random parameters model is by allowing the mean to vary across observations (i.e., assuming heterogeneous means of random parameters) [25][26][27][28] and the correlation of random parameters (i.e., introducing covariance of random parameters) [29][30][31]. Recent studies have proven that the goodness-of-fit, prediction accuracy, and explanatory analysis can be significantly enhanced by using the improved random parameters approach [25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…A more flexible way to improve the basic random parameters model is by allowing the mean to vary across observations (i.e., assuming heterogeneous means of random parameters) [25][26][27][28] and the correlation of random parameters (i.e., introducing covariance of random parameters) [29][30][31]. Recent studies have proven that the goodness-of-fit, prediction accuracy, and explanatory analysis can be significantly enhanced by using the improved random parameters approach [25][26][27][28][29][30][31]. Although the abovementioned random parameters approach has been increasingly adopted for either crash-frequency or injury-severity analysis to account for unobserved heterogamy, it has rarely been used for the joint analysis of crash frequency and injury severity (i.e., jointly modeling for crash frequency by severity level).…”

Section: Introductionmentioning

confidence: 99%

Joint Analysis of Crash Frequency by Severity Based on a Random Parameters Approach

Chen,

Xu,

2023

Sustainability

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Unobserved heterogeneity is a major challenge in estimating reliable road safety models. The random parameters approach has been proven to be an effective way to account for unobserved heterogeneity but has rarely been used for crash frequency by severity level. In this paper, a fixed parameters model, a basic random parameters model, and an improved random parameters model, allowing for heterogeneity in the means and correlation of random parameters, are estimated and comparatively evaluated. To quantitatively analyze the impact of explanatory variables on the crash frequency of various severity levels, the calculating method of marginal effects for estimated models is proposed. The results indicate that (1) the basic random parameters model statistically outperforms the fixed parameters model, and the statistical fit can be further improved by introducing heterogeneous means and correlation of random parameters; (2) for the predictive performance, the basic random parameters model is more accurate than the fixed parameters model, and the improved random parameters model can further reduce the mean error, mean absolute error, and root mean square error by 40–100%, 3.7–8.3%, and 7.6–8.9%, respectively; (3) ignoring the unobserved heterogeneity or neglecting the heterogeneity in the means and correlation of random parameters may result in biased safety inferences, and the maximum bias of marginal effects can easily exceed 100 percent; and (4) the safety effects of explanatory variables are thoroughly discussed and the potential safety countermeasures are provided. The random parameters approach and the method for calculating marginal effects proposed in this study are expected to provide a new methodological alternative for the joint analysis of crash frequency by severity and should be helpful in uncovering the mechanism of crash occurrence and the resulting injury severity accurately.

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“…It has been reported that motorcycle riders are at a 34-fold higher risk of fatal crashes than other car types [ 6 ]. Moreover, head injuries are the primary source of trauma and casualties for these riders [ 7 ]. Motorcycle injuries account for a significant proportion of all traffic casualties in Pakistan, and bike riders are most likely to experience severe injury among vulnerable road users.…”

Section: Introductionmentioning

confidence: 99%

Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances

Ijaz

Liu

Almarhabi

et al. 2022

IJERPH

Self Cite

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Not wearing a helmet, not properly strapping the helmet on, or wearing a substandard helmet increases the risk of fatalities and injuries in motorcycle crashes. This research examines the differences in motorcycle crash injury severity considering crashes involving the compliance with and defiance of helmet use by motorcycle riders and highlights the temporal variation in their impact. Three-year (2017–2019) motorcycle crash data were collected from RESCUE 1122, a provincial emergency response service for Rawalpindi, Pakistan. The available crash data include crash-specific information, vehicle, driver, spatial and temporal characteristics, roadway features, and traffic volume, which influence the motorcyclist’s injury severity. A random parameters logit model with heterogeneity in means and variances was evaluated to predict critical contributory factors in helmet-wearing and non-helmet-wearing motorcyclist crashes. Model estimates suggest significant variations in the impact of explanatory variables on motorcyclists’ injury severity in the case of compliance with and defiance of helmet use. For helmet-wearing motorcyclists, key factors significantly associated with increasingly severe injury and fatal injuries include young riders (below 20 years of age), female pillion riders, collisions with another motorcycle, large trucks, passenger car, drivers aged 50 years and above, and drivers being distracted while driving. In contrast, for non-helmet-wearing motorcyclists, the significant factors responsible for severe injuries and fatalities were distracted driving, the collision of two motorcycles, crashes at U-turns, weekday crashes, and drivers above 50 years of age. The impact of parameters that predict motorcyclist injury severity was found to vary dramatically over time, exhibiting statistically significant temporal instability. The results of this study can serve as potential motorcycle safety guidelines for all relevant stakeholders to improve the state of motorcycle safety in the country.

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Investigation of factors influencing motorcyclist injury severity using random parameters logit model with heterogeneity in means and variances

Cited by 28 publications

References 45 publications

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Joint Analysis of Crash Frequency by Severity Based on a Random Parameters Approach

Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances

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