Hierarchical binary logit model to compare driver injury severity in single-vehicle crash based on age-groups

Se, Chamroeun; Champahom, Thanapong; Jomnonkwao, Sajjakaj; Banyong, Chinnakrit; Sukontasukkul, Piti; Ratanavaraha, Vatanavongs

doi:10.1080/17457300.2020.1858113

Cited by 17 publications

(7 citation statements)

References 69 publications

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“…In contrast, crashes involving vehicles running off a curved roadway and hitting a roadside guardrail were found to increase the likelihood of minor injuries while decreasing the risk of fatal injuries for 2012–2013 restrained drivers, 2016–2017 restrained drivers, and 2016–2017 unrestrained driver models. This result is in line with previous studies and makes intuitive sense 53 , 64 , 104 , highlighting the crucial safety advantages of roadside guardrail protection in mitigating hazardous crash mechanisms such as rollover or overturn crashes, preventing vehicles from going off the road, absorbing crash energy, and reducing the consequences of driving errors on forgiving roads 105 . Lastly, crashes that involve a vehicle running over a traffic island were found to be significant in the 2016–2017 restrained driver and 2012–2013 and 2014–2015 unrestrained driver models.…”

Section: Resultssupporting

confidence: 91%

“…On the other hand, the effect vehicle’s seatbelt restraint system on the crash injury severity was also extensively explored in the previous studies and found positive safety effect of seatbelt on the outcome severity of the crashes while using it as an explanatory variable 4 , 17 , 44 , 64 . Only a few studies have hypothesized and proved that the effect of seatbelt use status may not be exogeneous, but may be endogenous to crash-related injury severity 5 , 65 .…”

Section: Literature Reviewmentioning

confidence: 99%

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Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes

Champahom

Wisutwattanasak

et al. 2023

Sci Rep

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Upon detecting a crash impact, the vehicle restraint system locks the driver in place. However, external factors such as speeding, crash mechanisms, roadway attributes, vehicle type, and the surrounding environment typically contribute to the driver being jostled within the vehicle. As a result, it is crucial to model unrestrained and restrained drivers separately to reveal the true impact of the restraint system and other factors on driver injury severities. This paper aims to explore the differences in factors affecting injury severity for seatbelt-restrained and unrestrained drivers involved in speeding-related crashes while accounting for temporal instability in the investigation. Utilizing crash data from Thailand between 2012 and 2017, mixed logit models with heterogeneity in means and variances were employed to account for multi-layered unobserved heterogeneity. For restrained drivers, the risk of fatal or severe crashes was positively associated with factors such as male drivers, alcohol influence, flush/barrier median roadways, sloped roadways, vans, running off the roadway without roadside guardrails, and nighttime on unlit or lit roads. For unrestrained drivers, the likelihood of fatal or severe injuries increased in crashes involving older drivers, alcohol influence, raised or depressed median roadways, four-lane roadways, passenger cars, running off the roadway without roadside guardrails, and crashes occurring in rainy conditions. The out-of-sample prediction simulation results are particularly significant, as they show the maximum safety benefits achievable solely by using a vehicle's seatbelt system. Likelihood ratio test and predictive comparison findings highlight the considerable combined impact of temporal instability and the non-transferability of restrained and unrestrained driver injury severities across the periods studied. This finding also demonstrates a potential reduction in severe and fatal injury rates by simply replicating restrained driver conditions. The findings should be of value to policymakers, decision-makers, and highway engineers when developing potential countermeasures to improve driver safety and reduce the frequency of severe and fatal speeding-related single-vehicle crashes.

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Section: Resultssupporting

confidence: 91%

Section: Literature Reviewmentioning

confidence: 99%

Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes

Champahom

Wisutwattanasak

et al. 2023

Sci Rep

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“…The findings indicated that the probability of fatal crashes is higher for younger (≤24 years) and older drivers (65-74 and ≥75) than other age cohorts. Recently, Se et al [57] employed the hierarchical binary logit technique to compare driver injury severity and found that roadside safety features (guardrails) significantly reduce fatal crashes among young drivers, but they did not expand the result around university campus areas specifically. Ulak et al [48] also applied a three-step spatial analysis on three urban counties in northwest Florida to investigate crashes involving aging drivers.…”

Section: Statistical Analysis Of Correlated Factorsmentioning

confidence: 99%

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

et al. 2022

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Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.; compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population.

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“…γ 00 is the intercept for or β 0j and γ k0 is the intercept for β kj . µ 0j and µ kj stand for the prediction errors in information at the road level and demonstrate the unique effect of road j. Kim et al [19], Huang et al [20], Xiao et al [40], Se et al [41], Champahom et al [42] provide for extended details on the calculation presented above.…”

Section: Y Ij =mentioning

confidence: 99%

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

et al. 2021

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Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.

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Hierarchical binary logit model to compare driver injury severity in single-vehicle crash based on age-groups

Cited by 17 publications

References 69 publications

Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes

Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

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