Identifying regions of excess injury risks associated with distracted driving: A case study in Central Ohio, USA.

This study examines the latent influence of spatial locations on the relative risks of crash injuries associated with distracted driving (DD) and identifies regions of excess risks for policy intervention. Using a sample of aggregated injury and fatal DD crash records for the period 2015-2019 across 1,024 census block groups in Central Ohio (i.e., the Columbus Metropolitan Area) in the United States, we investigate the role of latent effects along with several covariates such as land-use mix, sociodemographic features, and the built environment. To this end, we specifically leverage a full Bayesian hierarchical formulation with conditional autoregressive priors to account for uncertainty (i.e., spatially structured random effects) stemming from adjacent census block groups. Furthermore, we consider uncorrelated random effects from upper-level administrative units within which each block group is nested (i.e., census tracts and counties). Our analysis reveals that (1) addressing spatial correlation improves the model's performance, (2) block-group-level variability substantially explains the residual random fluctuation, and (3) intersection density appears negatively associated with the relative risks of crash injuries, while more diversified land use can increase injury risk. Based on these findings, we present spatial clusters with twice the relative risks compared to other block groups, suggesting that policies be devised to mitigate severe injuries due to DD and therefore enhance public health.

Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea.

This study aims to provide an improved understanding of the local-level spatiotemporal evolution of COVID-19 spread across capital regions of South Korea during the second and third waves of the pandemic (August 2020~June 2021). To explain transmission, we rely upon the local safety level indices along with latent influences from the spatial alignment of municipalities and their serial (temporal) correlation. Utilizing a flexible hierarchical Bayesian model as an analytic operational framework, we exploit the modified BYM (BYM2) model with the Penalized Complexity (PC) priors to account for latent effects (unobserved heterogeneity). The outcome reveals that a municipality with higher population density is likely to have an elevated infection risk, whereas one with good preparedness for infectious disease tends to have a reduction in risk. Furthermore, we identify that including spatial and temporal correlations into the modeling framework significantly improves the performance and explanatory power, justifying our adoption of latent effects. Based on these findings, we present the dynamic evolution of COVID-19 across the Seoul Capital Area (SCA), which helps us verify unique patterns of disease spread as well as regions of elevated risk for further policy intervention and for supporting informed decision making for responding to infectious diseases.

Local-level spatiotemporal dynamics of COVID-19 transmission in the Greater Seoul Area, Korea: a view from a Bayesian perspective.

OBJECTIVES: The purpose of this study was to enhance the understanding of the local-level spatiotemporal dynamics of COVID-19 transmission in the Greater Seoul Area (GSA), Korea, after its initial outbreak in January 2020. METHODS: Using the weekly aggregates of coronavirus disease 2019 (COVID-19) cases of 77 municipalities in the GSA, we examined the relative risks of COVID-19 infection across local districts over 50 consecutive weeks in 2020. To this end, we employed a spatiotemporal generalized linear mixed model under the hierarchical Bayesian framework. This allowed us to empirically examine the random effects of spatial alignments, temporal autocorrelation, and spatiotemporal interaction, along with fixed effects. Specifically, we utilized the conditional autoregressive and the weakly informative penalized complexity priors for hyperparameters of the random effects. RESULTS: Spatiotemporal interaction dominated the overall variability of random influences, followed by spatial correlation, whereas the temporal correlation appeared to be small. Considering these findings, we present dynamic changes in the spread of COVID-19 across local municipalities in the GSA as well as regions at elevated risk for further policy intervention. CONCLUSIONS: The outcomes of this study can contribute to advancing our understanding of the local-level COVID-19 spread dynamics within densely populated regions in Korea throughout 2020 from a different perspective, and will contribute to the development of regional safety planning against infectious diseases.

Exploring the effects of PM2.5 and temperature on COVID-19 transmission in Seoul, South Korea.

With a recent surge of the new severe acute respiratory syndrome-coronavirus 2 (SARS-Cov-2, COVID-19) in South Korea, this study attempts to investigate the effects of environmental conditions such as air pollutants (PM2.5) and meteorological covariate (Temperature) on COVID-19 transmission in Seoul. To account for unobserved heterogeneity in the daily confirmed cases of COVID-19 across 25 contiguous districts within Seoul, we adopt a full Bayesian hierarchical approach for the generalized linear mixed models. A formal statistical analysis suggests that there exists a positive correlation between a 7-day lagged effect of PM2.5 concentration and the number of confirmed COVID-19 cases, which implies an elevated risk of the infectious disease. Conversely, temperature has shown a negative correlation with the number of COVID-19 cases, leading to reduction in relative risks. In addition, we clarify that the random fluctuation in the relative risks of COVID-19 mainly originates from temporal aspects, whereas no significant evidence of variability in relative risks is observed in terms of spatial alignment of the 25 districts. Nevertheless, this study provides empirical evidence using model-based formal assessments regarding COVID-19 infection risks in 25 districts of Seoul from a different perspective.

Developing Crash Severity Model Handling Class Imbalance and Implementing Ordered Nature: Focusing on Elderly Drivers.

Along with the rapid demographic change, there has been increased attention to the risk of vehicle crashes relative to older drivers. Due to senior involvement and their physical vulnerability, it is crucial to develop models that accurately predict the severity of senior-involved crashes. However, the challenge is how to cope with an imbalanced severity class distribution and the ordered nature of crash severities, as these can complicate the classification of the severity of crashes. In that regard, this study investigates the influence of implementing ordinal nature and handling imbalanced class distribution on the prediction performance. Using vehicle crash data in Ohio, U.S., as an example, the eight machine learning classifiers (logistic and ordered logistic regressions and random forest and ordered random forest with or without handling imbalanced classes) are suggested and then compared with their respective performances. The analysis outcomes show that balancing strategy enhances performance in predicting severe crashes. In contrast, the effects of implementing ordinal nature vary across models. Specifically, the ordered random forest classifier without balancing appears to be superior in terms of overall prediction accuracy, and the ordered random forest with balancing outperforms others in predicting severer crashes.

Influence of built environment on the severity of vehicle crashes caused by distracted driving: A multi-state comparison.

With recent increased attention to the consequences of distracted driving (DD), this research provides a comprehensive investigation of the role of the built environment on the severity of vehicle crashes caused by DD. Utilizing crash data collected from fifteen states in the United States for the period 2013-2017, the association between distracted driving crash severity and various built environment indicators was examined by the generalized ordered logit regression model. The results show that at a lower severity level, DD related crashes were found to be less severe at roundabouts or in urban areas, whereas the probability of injuries rather than property damage only (PDO) increases if an accident involves speeding or when occurring at an intersection or a curved road. Comparatively, at a higher severity level, the odds of severe (or fatal) injury involvement compared to minor injuries and PDO was found to be higher in a work-zone, a curved roadway, or when excessive speed was involved. Conversely, roundabouts and urban areas affected negatively in severe DD crash, which is consistent with the lower-level case. The study also reveals a state-specific variability of the influence of the built environment on the severity of DD related crashes. These findings provide a comprehensive understanding of the severity of DD related crashes for transportation safety planners or policymakers to develop customized policy recommendations, such as designing policies or roadway safety treatments, to curb the negative consequences of distracted driving.

Does space influence on the frequency and severity of the distraction-affected vehicle crashes? An empirical evidence from the Central Ohio.

This study investigates spatial dependencies between frequency and within severity of vehicle crashes caused by distracted driving, along with the role of the built and socio-demographic environments in the Columbus Metropolitan Area, Ohio. We adopt a full Bayesian hierarchical framework with Multivariate Conditional Autoregressive Priors to account for the complex spatial correlation structure as well as the unobserved heterogeneity. Using aggregated crash count data (Property Damage Only and Bodily Injuries) for the 414 census tracts, the analysis outcomes reveal that census tracts providing more jobs and having a higher proportion of commercial land use would have higher likelihood of relative crash risks in both severity levels. Inclusion of correlation structure between frequency as well as within crash-severity-level has proven a significant increase on the performance of the model, verifying influences of space on the frequency and severity of distraction-affected vehicle crashes. In addition, this research presents areas of higher relative risks (spatial clusters) that have 1.5 times elevated risk of collision than other census tracts. The identification of areas of excessive risks informs us to devise policies to mitigate negative consequences of distraction-affected crashes.