Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas

The Association Between Home and Crash Site Social Vulnerability on Injury and Mortality After Motor Vehicle Crashes: Implications for Traffic Policy

INTRODUCTION

There is a growing body of literature that shows geographic social vulnerability, which seeks to measure the resiliency of a community to withstand unforeseen disasters, may be associated with negative outcomes after traumatic injury. For motor vehicle collisions (MVCs) specifically, it is unknown how the resources of a patient's home environment may interact with resources of the environment where the crash occurred.

METHODS

We merged publicly available crash data from the state of Michigan with the Michigan Trauma Quality Improvement dataset. A social vulnerability index (SVI) score was calculated for each ZIP code and was then cross-referenced between the location of the MVC (Crash-SVI) and the patient's home address (Home-SVI). SVI was divided into quintiles, with higher numbers indicating greater vulnerability. Adjusted logistic regression models using least absolute shrinkage and selection operator for feature selection and regularization were performed sequentially using patient, vehicular, and environmental variables to identify associations between Home-SVI and Crash-SVI, with mortality and injury severity score (ISS) greater than 15 (ISS15).

RESULTS

Between January 2020 and December 2022, a total of 14,706 patients were identified. Most MVCs (75.3% of all patients) occurred in the second through fourth quintiles of SVI. In all cases, Crash-SVI occurred most frequently within the same quintile as the patient's Home-SVI. Average crash speed limits showed a significant negative association with increasing SVI. On adjusted logistic regression, there were significantly increased odds of mortality for the fifth quintile of Home-SVI in comparison to the first quintile when adjusted for patient factors; but this lost significance after the addition of vehicular or environmental variables. In contrast, there were decreased odds of ISS15 for the highest quintiles of Crash-SVI in all logistic regression models.

CONCLUSIONS

Geographic social vulnerability markers were associated with lower MVC-associated injury severity, perhaps in part because of the association with lower speed limit in these areas.

Designing user-centered decision support systems for climate disasters: What information do communities and rescue responders need during floods?

Flooding events are the most common natural hazard globally, resulting in vast destruction and loss of life. An effective flood emergency response is necessary to lessen the negative impacts of flood disasters. However, disaster management and response efforts face a complex scenario. Simultaneously, regular citizens attempt to navigate the various sources of information being distributed and determine their best course of action. One thing is evident across all disaster scenarios: having accurate information and clear communication between citizens and rescue personnel is critical. This research aims to identify the diverse needs of two groups, rescue operators and citizens, during flood disaster events by investigating the sources and types of information they rely on and information that would improve their responses in the future. This information can improve the design and implementation of existing and future spatial decision support systems (SDSSs) during flooding events. This research identifies information characteristics crucial for rescue operators and everyday citizens' response and possible evacuation to flooding events by qualitatively coding survey responses from rescue responders and the public. The results show that including local input in SDSS development is crucial for improving higher-resolution flood risk quantification models. Doing so democratizes data collection and analysis, creates transparency and trust between people and governments, and leads to transformative solutions for the broader scientific community.

Injury-severity analysis of crashes involving defective vehicles and accounting for the underlying socioeconomic mediators

Crashes occur from a combination of factors related to the driver, roadway, and vehicle factors. The impact of vehicles on road crashes is a critical consideration within road safety analysis, even though not much studies have been conducted in this area. This study assessed how various vehicle and other crash factors are significantly associated with crash outcomes. To do this, historical vehicle defect-related crashes were obtained for the state of Alabama from 2016 to 2020. After data cleaning, a crash injury severity model was developed using the random parameters multinomial logit with heterogeneity in means approach to account for possible unobserved heterogeneity in the data. A spatial analysis was further conducted to better understand vehicle defect crashes as a broader societal issue and potentially explore their connection with the socio-demographic characteristics of the drivers of these vehicles. The preliminary data analysis showed that brake and tire defects accounted for about 65% of the vehicle defects associated with the crashes. The model estimation results revealed that improper tread depth and headlight defects were associated with major injury outcomes, while brake defects were more associated with minor injuries. Also, crashes associated with speeding, drunk driving, failure to use seatbelts, and those that occurred on curved roads left with downgrades were likely to result in major injuries. Findings from the spatial analysis showed that postal codes with higher median incomes are more likely to record lower vehicle defect-related crashes, unlike those that have higher proportions of females and African Americans. The study's findings provide data-driven evidence for sustained safety campaigns, workshops, and training on basic vehicle maintenance practices in the low-income communities in the state.

Decoding the impacts of contributory factors and addressing social disparities in crash frequency analysis

Understanding the relationship between social disparities and traffic crash frequency is essential for long-term transportation planning and policymaking. Few studies have systemically examined the influence of socioeconomic and infrastructure-related disparities in macro-level traffic crash frequency. This study provides a framework to spatially examine the relationships between crash rates and demographic and socioeconomic characteristics, as well as roadway infrastructure and traffic characteristics at the Census Block Groups (CBGs) level. Spatial autocorrelation analysis was first performed on the residual of the Ordinary Least Squares (OLS) model to identify whether non-stationarity exists. Then, the Geographically Weighted Regression (GWR) model and the Multiscale Geographically Weighted Regression (MGWR) model were applied to assess the impacts of these factors on crash rates spatially and statistically. Our findings indicate that MGWR outperforms both OLS and GWR in uncovering the spatial relationships between contributing factors and both fatal and injury (FI) crashes as well as property damage only (PDO) crashes. A thorough examination of local coefficient maps highlighted six pivotal variables that significantly influenced a majority of CBGs. Improving infrastructure, including pedestrian pathways and public transit facilities, in low-income areas can offer significant benefits. These findings and recommendations can inform the development of effective strategies for reducing crashes and guide the appropriate selection of modeling techniques for macro-level crash analysis.

Investigating transportation safety in disadvantaged communities by integrating crash and Environmental Justice data

Recent efforts to identify disadvantaged communities (DACs) on a census tract level have evoked possibilities of attaining transportation justice and vision zero goals in these areas. To identify DACs, the United States Department of Transportation (USDOT) has developed six comprehensive indicators: economy, environment, equity, health, resilience, and transportation access. The indicators are used to explore the associations between DACs (in 71,728 census tracts) and five years of fatal crashes, providing a comprehensive understanding of safety risks. Specifically, using data on DACs and linking it with census and crash data, this study aims to understand the complex connections between safety (captured through fatal crashes) and disadvantages that communities confront due to a convergence of multiple challenges and burdens using Zero-Hurdle Negative Binomial models. The results reveal that health, resilience, and transportation-disadvantaged tracts are associated with more fatal crashes. The study also found the presence of a higher percentage of the population with bachelor's degrees and increased use of public transportation are correlated with fewer fatal crashes. Also, a higher fatal crash rate is observed in disadvantaged census tracts where a high proportion of the Hawaiian or other Pacific Islander, and American Indian or Alaska Native populations live. This implies that targeted interventions can be explored further in tracts that show high correlations with fatal crashes. The findings contribute to traffic safety by highlighting the risks in DACs, which can help design and implement traffic safety interventions. The insights gained from this study can inform decision-making and help to guide the development of more equitable traffic safety programs in disadvantaged communities.

How do people perceive driving risks in small towns? A case study in Central Texas

The number of studies investigating the relationship between perceived and objective traffic risk from drivers' perspective is limited. This study aims to investigate this dynamic within an understudied transportation environment - small towns in Texas, USA, defined as incorporated places with a population of less than 50,000. A web-based survey was distributed to six small towns in central Texas to ascertain perceptual traffic risk factors and personal characteristics. A participatory GIS exercise was also conducted to collect where high-risk locations were perceived and to correlate them to high crash zones. This study spatially examined the relations between perceived and observed risk locations and statistically identified a set of contributing factors which could make crash-intensive areas more perceivable by road users. The results indicated that road users' perceived risk locations are not always associated with high crash rates. The match rate between perceived and observed risk locations varied significantly across studied sites. We found that some personal and built environment factors significantly impacted people's sensitivity to perceiving crash-intensive locations. The binary logistic regression model was accurate (74.13%) in highlighting whether a perceived risk location matches observed risk locations. The results emphasize the importance of considering perceived and objective risk simultaneously to gain a better understanding of traffic risk mitigation, especially in underserved small towns.

Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression

Previous researches have demonstrated that traffic crashes in urban areas are geographical events and strongly linked to local characteristics such as road network and land attributes. However, with a significant emphasis on moving-vehicle crashes, the spatial pattern of fixed-object crashes is unclear so far. The difference between these two types of crashes, and whether existing spatial tools such as geographically weighted regression can interpretate the occurrence mode have not been investigated before. To fill this gap, this paper focuses on understanding the spatial features and occurrence of these two types of crash, i.e., moving-vehicle and fixed-object on the city level. Crash data from Dalian, China were aggregated into subdistricts and calibrated with multi-scale geographically weighted regression (MGWR) models. A noticeable but similar clustering pattern was revealed in both types, with spatial overlap of their accident-prone regions. The spatial influence of explanatory variables (road network, geographic, demographic, socio-economic, and land-use variables) was also found mostly similar in both types of crashes. However, fixed-object crash in downtown is more affected by node count, while POI entrance/exit count, especially those in areas with more industrial zones tend to significantly reduce crash risk. In both types of crashes, terrain slope rather than elevation is found to mitigate the crash risk, especially in the downtown area. Compared to traditional Geographically Weighted Regression (GWR) with a fixed bandwidth, the improvement in modeling performance using MGWR highlights the reasonability and benefits to consider the influence scale of each contributing factor in urban spatial analysis of traffic collisions. This study could help transportation authorities identify high-risk regions, understand their contributing factors and take precautions for improving the local traffic safety.