Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes

Alnawmasi, Nawaf; Ali, Yasir; Yasmin, Shamsunnahar

doi:10.1016/j.aap.2023.107339

Cited by 18 publications

(4 citation statements)

References 60 publications

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“…While these groups have always faced certain risks, the post-2020 period saw a marked increase in crash severity involving them. This change is possibly due to a pandemic-induced shift in transportation modes, with more individuals opting for walking, biking, and motorcycles, leading to increased severity in related crashes [19,23,38,39,[42][43][44][45][46][47].…”

Section: Discussionmentioning

confidence: 99%

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

Alrumaidhi,

Rakha

2024

Sustainability

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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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Section: Discussionmentioning

confidence: 99%

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

Alrumaidhi,

Rakha

2024

Sustainability

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“…Roads with more facilities tend to experience fewer severe accidents. Intersections are more prone to severe or fatal compared to other sections of the road [50] [51]. Traffic signals play a pivotal role in diminishing the likelihood of fatal injuries by minimizing the occurrence of side-impact crashes [52].…”

Section: Explanation Comparisons With Other Methodsmentioning

confidence: 99%

Explaining Multiple Types of Crash Injury Severity Predictions with Layer-wise Relevance Propagation in Multi-task Deep Neural Networks

Xiao,

Duan,

Lei

2024

Preprint

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Accurately predicting the severity of traffic accidents is crucial for preventing them and safeguarding traffic safety. Practitioners need to understand the underlying predictive mechanisms to identify associated risk factors and develop appropriate interventions effectively. Unfortunately, existing research often falls short in predicting diverse outcomes, with some studies neglecting the latter entirely. Moreover, designing explainable deep neural networks (DNNs) is challenging, unlike traditional models, which makes it difficult to achieve explainability with DNNs that incorporate neural networks. We propose a multi-task deep neural network framework designed to predict different types of injury severity, including injury, fatality, and property damage. Our proposed approach offers a thorough and precise method for analyzing crash injury severity. Unlike black-box models, our framework can pinpoint the critical factors contributing to injury severity by employing improved layer-wise relevance propagation. Experiments on Chinese traffic accidents demonstrate that our model accurately predicts the factors associated with injury severity and surpasses existing methods. Moreover, our experiments reveal that the critical factors identified by our approach are more logical and informative compared to those provided by baseline models. Additionally, our findings can assist policymakers make more enlightened decisions when devising and implementing improvements in traffic safety.

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“…Xu et al (2014) [33] similarly found statistical support for Markov transformations in injury severity models using detailed data conditioned on crash occurrence. In addition, with the advancement of modeling methods, more methods for portraying temporal heterogeneity have emerged, including Generalized Estimating Equations (GEE) [14,15], Autoregressive Models (AM) [34], Autoregressive Moving Average Models (AMAM) [35], and Integer-valued Autoregressive Poisson models (IVAP) [36]. The common feature of these methods is to characterize the instability of risk effects by embedding correlation functions with lagged effects to portray the relationship between sample data at different time periods.…”

Section: Temporal Correlations In Crash Datamentioning

confidence: 99%

“…Specifically, because crashes are relatively rare events, analysts typically aggregate crash data over time (months, quarters, or years) to provide a sufficient number of observations for statistical analysis. Thus, the time interval between two crash observations implies an assumption of temporal stability, which has far-reaching implications for the construction of SPFs and the analysis of CMFs [15].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Crash Modification Factors (CMFs) in Mountain Freeways: Considering Temporal Instability in Crash Data

Zhang,

Huang,

Kuang

et al. 2024

Sustainability

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The combined contributions to mountain freeway safety of pavement performance, weather conditions, and traffic condition indicators have not been thoroughly investigated due to the complexity of their interactions and temporal instability. A cross-sectional analysis using a Generalized Linear Model (GLM) approach with negative binomial distribution considering time-correlation effects (TC-NB) was adopted to estimate the Crash Modification Factors (CMFs) of these indicators for different segment types, alignment types, and cross-sectional forms based on eight quarters of data from mountain freeways in China. According to the results, improving the pavement performance indexes positively impacts the safety of different freeway segments, especially for the curved segments. Quarterly Average Daily Traffic (QADT) has significantly negative safety effects on two-lane segments with relatively narrow spaces, while the proportion of large vehicles plays a decisive role in the safety impacts of tunnel segments. Small/moderate rain days in a quarter (SMR) were significantly positively correlated with crash frequency, while the percentage of torrential rain days in a quarter (TR) showed an opposite trend. The results of this study contribute to the effective coordination of traffic monitoring systems, pavement management systems, and traffic safety management systems to develop targeted improvement countermeasures for different freeway section types.

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Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes

Cited by 18 publications

References 60 publications

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

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

Explaining Multiple Types of Crash Injury Severity Predictions with Layer-wise Relevance Propagation in Multi-task Deep Neural Networks

Estimation of Crash Modification Factors (CMFs) in Mountain Freeways: Considering Temporal Instability in Crash Data

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