Factors Affecting Crash Severity among Elderly Drivers: A Multilevel Ordinal Logistic Regression Approach

Abstract

This study modeled the crash severity of elderly drivers using data from the state of Virginia, United States, for the period of 2014 through to 2021. The impact of several exogenous variables on the level of crash severity was investigated. A multilevel ordinal logistic regression model (M-OLR) was utilized to account for the spatial heterogeneity across different physical jurisdictions. The findings discussed herein indicate that the M-OLR can handle the spatial heterogeneity and lead to a better fit in comparison to a standard ordinal logistic regression model (OLR), as the likelihood-ratio statistics comparing the OLR and M-OLR models were found to be statistically significant, with p-value of <0.001. The results showed that crashes occurring on two-way roads are likely to be more severe than those on one-way roads. Moreover, the risks for older, distracted, and/or drowsy drivers to be involved in more severe crashes escalate than undistracted and nondrowsy drivers. The data also confirmed that the consequences of crashes involving unbelted drivers are prone to be more severe than those for belted drivers and their passengers. Furthermore, the crash severity on higher-speed roads or when linked to high-speed violations is more extreme than on low-speed roads or when operating in compliance with stated speed limits. Crashes that involve animals are likely to lead to property damage only, rather than result in severe injuries. These findings provide insights into the contributing factors for crash severity among older drivers in Virginia and support better designs of Virginia road networks.

An Econometric Analysis to Explore the Temporal Variability of the Factors Affecting Crash Severity Due to COVID-19

“…Typically, these data can be categorized into levels such as "no injury", "minor injury", and "severe injury". Ordinal logistic regression (OLR) was chosen for this study due to its suitability for modeling ordinal outcome variables [9,24]. The basic idea behind OLR is to model the cumulative probabilities of observing an outcome up to and including a particular category.…”

“…In many traffic safety studies, crash data are clustered or grouped by nature. For instance, crashes might occur within specific intersections, along specific road segments, or within certain jurisdictions [9,36]. This hierarchical structure can introduce dependencies among the ob-servations within the same group, violating the assumption of observations' independence in traditional regression models [9,36] To account for this clustered structure, a multilevel ordinal logistic regression (M-OLR) with a random intercept was employed [9].…”

“…For instance, crashes might occur within specific intersections, along specific road segments, or within certain jurisdictions [9,36]. This hierarchical structure can introduce dependencies among the ob-servations within the same group, violating the assumption of observations' independence in traditional regression models [9,36] To account for this clustered structure, a multilevel ordinal logistic regression (M-OLR) with a random intercept was employed [9]. This approach extends traditional ordinal logistic regression by adding random effects to capture the variability between clusters, providing more accurate results [9].…”

“…However, these advancements have also ushered in challenges. The rising frequency and severity of road traffic crashes have become major concerns [8][9][10]. Despite advancements in safety technologies and regulatory measures, these crashes remain a pressing issue, affecting health outcomes and having broader socio-economic implications [8,9,[11][12][13].…”

“…The rising frequency and severity of road traffic crashes have become major concerns [8][9][10]. Despite advancements in safety technologies and regulatory measures, these crashes remain a pressing issue, affecting health outcomes and having broader socio-economic implications [8,9,[11][12][13]. The World Health Organization, among other institutions, has consistently highlighted road traffic crashes as a global health risk [12].…”