ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
The COVID-19 pandemic and related restrictions led to major transit demand decline for many public transit systems in the United States. This paper is a systematic analysis of the dynamics and dimensions of this unprecedented decline. Using transit demand data derived from a widely used transit navigation app, we fit logistic functions to model the decline in daily demand and derive key parameters: base value, the apparent minimal level of demand and cliff and base points, representing the initial date when transit demand decline began and the final date when the decline rate attenuated. Regression analyses reveal that communities with higher proportions of essential workers, vulnerable populations (African American, Hispanic, Female, and people over 45 years old), and more coronavirus Google searches tend to maintain higher levels of minimal demand during COVID-19. Approximately half of the agencies experienced their decline before the local spread of COVID-19 likely began; most of these are in the US Midwest. Almost no transit systems finished their decline periods before local community spread. We also compare hourly demand profiles for each system before and during COVID-19 using ordinary Procrustes distance analysis. The results show substantial departures from typical weekday hourly demand profiles. Our results provide insights into public transit as an essential service during a pandemic.