Lower Volumes, Higher Speeds: Changes to Crash Type, Timing, and Severity on Urban Roads from COVID-19 Stay-at-Home Policies.

Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio's Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.

Using GIS to Understand the Influence of Hurricane Harvey on Spatial Access to Primary Care.

Hurricanes can have a significant impact on the functioning and capacity of healthcare systems. However, little work has been done to understand the extent to which hurricanes influence local residents' spatial access to healthcare. Our study evaluates the change in spatial access to primary care physicians (PCPs) between 2016 and 2018 (i.e., before and after Hurricane Harvey) in Harris County, Texas. We used an enhanced 2-step floating catchment area (E2SFCA) method to measure spatial access to PCPs at the census tract level. The results show that, despite an increased supply of PCPs across the county, most census tracts, especially those in the northern and eastern fringe areas, experienced decreased access during this period as measured by the spatial access ratio (SPAR). We explain this decline in SPAR by the shift in the spatial distribution of PCPs to the central areas of Harris County from the fringe areas after Harvey. We also examined the socio-demographic impact in the SPAR change and found little variation in change among different socio-demographic groups. Therefore, public health professionals and disaster managers may use our spatial access measure to highlight the geographic disparities in healthcare systems. In addition, we recommend considering other social and institutional dimensions of access, such as users' needs, preferences, resource capacity, mobility options, and quality of healthcare services, in building a resilient and inclusive post-hurricane healthcare system.

Public transit cuts during COVID-19 compound social vulnerability in 22 US cities.

The COVID-19 pandemic has severely impacted public transit services through plummeting ridership during the lockdown and subsequent budget cuts. This study investigates the equity impacts of reductions in accessibility due to transit service cuts during COVID-19 and their association with urban sprawl. We evaluated transit access to food and health care services across 22 US cities in three phases during 2020. We found stark socio-spatial disparities in access to basic services and employment in food and health care. Transit service cuts worsened accessibility for communities with multiple social vulnerabilities, such as neighborhoods with high rates of poverty, low-income workers, and zero-vehicle households, as well as poor neighborhoods with high concentrations of black residents. Moreover, sprawled cities experienced greater access loss during COVID-19 than compact cities. Our results point to policies and interventions to maintain social equity and sustainable urban development while benefiting diverse social groups during disruptions.

COVID-19 exacerbates unequal food access.

Inequality to food access has always been a serious problem, yet it became even more critical during the COVID-19 pandemic, which exacerbated social inequality and reshaped essential travel. This study provides a holistic view of spatio-temporal changes in food access based on observed travel data for all grocery shopping trips in Columbus, Ohio, during and after the state-wide stay-at-home period. We estimated the decline and recovery patterns of store visits during the pandemic to identify the key socio-economic and built environment determinants of food shopping patterns. The results show a disparity: during the lockdown, store visits to dollar stores declined the least, while visits to big-box stores declined the most and recovered the fastest. Visits to stores in low-income areas experienced smaller changes even during the lockdown period. A higher percentage of low-income customers was associated with lower store visits during the lockdown period. Furthermore, stores with a higher percentage of white customers declined the least and recovered faster during the reopening phase. Our study improves the understanding of the impact of the COVID-19 crisis on food access disparities and business performance. It highlights the role of COVID-19 and similar disruptions on exposing underlying social problems in the US.