Ten Takeaways from the COVID-19 Pandemic for Transportation Planners

The COVID-19 pandemic has created significant challenges but also unprecedented opportunities for transportation researchers and practitioners. In this article, the major lessons and gaps in knowledge for those working in the transportation sector are identified, including the following: (1) integration between public health and transportation;(2) technology to support contact tracing and tracking of travelers;(3) focus on vulnerable, at-risk operators, patrons, and underserved members of society;(4) re-engineering of travel demand models to support social distancing, quarantine, and public health interventions;(5) challenges with Big Data and information technologies;(6) trust relationships between the general public, government, private sector, and others in disaster management;(7) conflict management during disasters;(8) complexities of transdisciplinary knowledge and engagement;(9) demands for training and education;and (10) transformative change to support community resilience. With a focus on transportation planning and community resilience, the lessons from the pandemic need to be shared and customized for different systems, services, modalities, and users. While many of the interventions during the pandemic have been based on public health, the management, response, recovery, adaptation, and transformation of transportation systems resulting from the crisis require multi-disciplinary, multi-jurisdictional communications and coordination, and resource sharing. Further research to support knowledge to action is needed.

Ten Takeaways from the COVID-19 Pandemic for Transportation Planners.

The COVID-19 pandemic has created significant challenges but also unprecedented opportunities for transportation researchers and practitioners. In this article, the major lessons and gaps in knowledge for those working in the transportation sector are identified, including the following: (1) integration between public health and transportation; (2) technology to support contact tracing and tracking of travelers; (3) focus on vulnerable, at-risk operators, patrons, and underserved members of society; (4) re-engineering of travel demand models to support social distancing, quarantine, and public health interventions; (5) challenges with Big Data and information technologies; (6) trust relationships between the general public, government, private sector, and others in disaster management; (7) conflict management during disasters; (8) complexities of transdisciplinary knowledge and engagement; (9) demands for training and education; and (10) transformative change to support community resilience. With a focus on transportation planning and community resilience, the lessons from the pandemic need to be shared and customized for different systems, services, modalities, and users. While many of the interventions during the pandemic have been based on public health, the management, response, recovery, adaptation, and transformation of transportation systems resulting from the crisis require multi-disciplinary, multi-jurisdictional communications and coordination, and resource sharing. Further research to support knowledge to action is needed.

Factors Associated with Differences in Pandemic Preparedness and Response: Findings from a Nationwide Survey in the United States

Using data from a national survey conducted in the United States during the Spring of 2020, the differences between emergency managers, transportation planners, and others involved in pandemic disaster response in terms of risk perception and protective actions are investigated.  The study found that 92 percent of respondents reported implementing voluntary actions with 35 percent reporting quarantine and 37 percent reporting isolation actions. The attributes of respondents and the agencies and communities they work in are categorized in terms of personal, disciplinary, or professional backgrounds, as attributes such as urban versus rural, coastal versus non-coastal, and other factors.  Three dependent variables are modeled including  1) risk tolerance;2) level of preparedness (including support for training), and 3) implementation of protective measures for social distancing, quarantine, and isolation to ascertain the influences of personal, professional, and regional, locational characteristics. A risk tolerance score is implemented by asking respondents “what percentage of the population would need to be sick to implement voluntary and non-voluntary actions. Using Poisson regression analysis and correspondence analysis, the patterns, associations, and clustering of backgrounds and other attributes are modeled to show the relationships between risk perceptions, preparedness, and professional backgrounds. In addition to identifying which places and people are more inclined to support protective actions for the pandemic, this analysis also helps to demonstrate the intersections and mutual interests across public health, transportation, and emergency management.  Overall, this study found a low level of preparedness for the pandemic with 70 percent of the respondents supporting additional training.

Pausing the Pandemic: Understanding and Managing Traveler and Community Spread of COVID-19 in Hawaii

In the absence of a vaccine, non-pharmaceutical interventions such as social distancing and travel reductions have become the only strategies for slowing the spread of the COVID-19 pandemic.  Using survey data from Hawaii (n = 22,200) collected in March through May of 2020 at the onset of the pandemic, the differences between traveler spreaders, who brought the disease into the state and community spreaders are investigated.  In addition to describing the demographic attributes of these two groups and comparing them to others vulnerable to COVID-19 disease, using logistic and multivariate regression models, characteristics, travel behaviors, and transport modes are examined. Traveler spreaders are likely to be male, younger, and likely to be returning students (not classified as employed) while community spreaders are also more likely to be male, essential workers, first responders, and medical personnel at the highest risk of exposure. A risk of infection score is also derived and analyzed to identify and assess attributes of individuals most likely to contract the disease. Using spatial statistics, hotspots, and clusters of locations of high-risk individuals are mapped and analyzed. The analysis supports efforts to better understand, respond, and slow the spread of the pandemic. Transportation researchers provide critical analytical capabilities and experience with relevant databases on mobility and the spread of infectious diseases.