Scholarly Works, Virginia Tech Transportation Institute

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https://hdl.handle.net/10919/89575

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Browsing Scholarly Works, Virginia Tech Transportation Institute by Author "Alrumaidhi, Mubarak"

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    Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques
    Alrumaidhi, Mubarak; Farag, Mohamed M. G.; Rakha, Hesham A. (MDPI, 2023-06-21)
    As the global elderly population continues to rise, the risk of severe crashes among elderly drivers has become a pressing concern. This study presents a comprehensive examination of crash severity among this demographic, employing machine learning models and data gathered from Virginia, United States of America, between 2014 and 2021. The analysis integrates parametric models, namely logistic regression and linear discriminant analysis (LDA), as well as non-parametric models like random forest (RF) and extreme gradient boosting (XGBoost). Central to this study is the application of resampling techniques, specifically, random over-sampling examples (ROSE) and the synthetic minority over-sampling technique (SMOTE), to address the dataset’s inherent imbalance and enhance the models’ predictive performance. Our findings reveal that the inclusion of these resampling techniques significantly improves the predictive power of parametric models, notably increasing the true positive rate for severe crash prediction from 6% to 60% and boosting the geometric mean from 25% to 69% in logistic regression. Likewise, employing SMOTE resulted in a notable improvement in the non-parametric models’ performance, leading to a true positive rate increase from 8% to 36% in XGBoost. Moreover, the study established the superiority of parametric models over non-parametric counterparts when balanced resampling techniques are utilized. Beyond predictive modeling, the study delves into the effects of various contributing factors on crash severity, enhancing the understanding of how these factors influence elderly road safety. Ultimately, these findings underscore the immense potential of machine learning models in analyzing complex crash data, pinpointing factors that heighten crash severity, and informing targeted interventions to mitigate the risks of elderly driving.
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    An Econometric Analysis to Explore the Temporal Variability of the Factors Affecting Crash Severity Due to COVID-19
    Alrumaidhi, Mubarak; Rakha, Hesham A. (MDPI, 2024-02-01)
    This study utilizes multilevel ordinal logistic regression (M-OLR), an approach that accounts for spatial heterogeneity, to assess the dynamics of crash severity in Virginia, USA, over the years 2018 to 2023. This period was notably influenced by the COVID-19 pandemic and its associated stay-at-home orders, which significantly altered traffic behaviors and crash severity patterns. This study aims to evaluate the pandemic’s impact on crash severity and examine the consequent changes in driver behaviors. Despite a reduction in total crashes, a worrying increase in the proportion of severe injuries is observed, suggesting that less congested roads during the pandemic led to riskier driving behaviors, notably increased speed violations. This research also highlights heightened risks for vulnerable road users such as pedestrians, cyclists, and motorcyclists, with changes in transportation habits during the pandemic leading to more severe crashes involving these groups. Additionally, this study emphasizes the consistent influence of environmental and roadway features, like weather conditions and traffic signals, in determining crash outcomes. These findings offer vital insights for road safety policymakers and urban planners, indicating the necessity of adaptive road safety strategies in response to changing societal norms and behaviors. The research underscores the critical role of individual behaviors and mental states in traffic safety management and advocates for holistic approaches to ensure road safety in a rapidly evolving post-pandemic landscape.
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    Factors Affecting Crash Severity among Elderly Drivers: A Multilevel Ordinal Logistic Regression Approach
    Alrumaidhi, Mubarak; Rakha, Hesham A. (MDPI, 2022-09-14)
    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.