Human behavior is notoriously difficult to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring about long-term behavioral changes. During the pandemic, people have been forced to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. A critical question going forward is how these experiences have actually changed preferences and habits in ways that might persist after the pandemic ends. Many observers have suggested theories about what the future will bring, but concrete evidence has been lacking. We present evidence on how much US adults expect their own postpandemic choices to differ from their prepandemic lifestyles in the areas of telecommuting, restaurant patronage, air travel, online shopping, transit use, car commuting, uptake of walking and biking, and home location. The analysis is based on a nationally representative survey dataset collected between July and October 2020. Key findings include that the “new normal” will feature a doubling of telecommuting, reduced air travel, and improved quality of life for some.

No abstract

This study identifies differences in COVID-19 related attitudes and risk perceptions among urban, rural, and suburban populations in the US using data from an online, nationwide survey collected during April-October 2020. In general, rural respondents were found to be less concerned by the pandemic and a lower proportion of rural respondents support staying at home and shutting down businesses. While only about half of rural respondents are concerned about getting severe reactions themselves from COVID-19 (compared to ~60% for urban and suburban respondents), all place types respondents are concerned about friends or family members getting severe reactions (~75%).

No abstract

Pedestrian infrastructure that is comfortable, connected to destinations of interest, and accessible to those with disabilities is vital to a safe, accessible, equitable, and sustainable transportation system. Planners recognize the benefits of providing well-maintained sidewalks and curb ramps, but often lack the asset management systems necessary to inventory sidewalk maintenance problems, prioritize sidewalk maintenance needs, and track the implementation of sidewalk improvement projects. Communities that are managing sidewalk presence and condition data typically link the data to their roadway network, which makes tracking specific sidewalk assets difficult. This paper introduces an affordable, semi-automated, and easy-to-implement process to generate a GIS-based sidewalk network with associated links and nodes representing crosswalks and intersections. Quantitative sidewalk condition data can be loaded onto the network, which allows it to be used to manage sidewalks as transportation assets, assessing pedestrian accessibility, prioritizing repairs or improvements, and to automatically identify accessible routes between origins and destinations. System inputs include parcel-level land-use and roadway centerline data, both of which are publicly available and free in most cases. The network is generated within the ArcGIS environment, using Python scripts to implement embedded ArcGIS functions. The method requires few computational resources, and tremendously reduces the manual labor required to develop a fully interconnected sidewalk network. Examples from multiple communities are presented to show how quantitative sidewalk condition data are loaded onto the network, and illustrate the network’s potential for pedestrian navigation and routing applications.

2Because of a recent federal initiative, states are now required (as of June 2008) to collect and 3 submit motorcycle VMT data to the FHWA. These data are needed to obtain better counts of 4 motorcycles to evaluate their impact on crashes and traffic flow. However, there is concern 5 about the quality of data submitted. Many states have identified problems with using automatic 6 traffic recorders to account for motorcycle traffic. Existing sensors exhibit difficulties in 7 counting motorcycles that travel side by side or close behind each other, they have difficulty in 8 distinguishing larger motorcycles from passenger vehicles, and magnetic counters in particular 9 do not sense motorcycles that do not pass over or travel close enough to the sensor.

The COVID-19 pandemic has impacted billions of people around the world. To capture some of these impacts in the United States, we are conducting a nationwide longitudinal survey collecting information about activity and travel-related behaviors and attitudes before, during, and after the COVID-19 pandemic. The survey questions cover a wide range of topics including commuting, daily travel, air travel, working from home, online learning, shopping, and risk perception, along with attitudinal, socioeconomic, and demographic information. The survey is deployed over multiple waves to the same respondents to monitor how behaviors and attitudes evolve over time. Version 1.0 of the survey contains 8,723 responses that are publicly available. This article details the methodology adopted for the collection, cleaning, and processing of the data. In addition, the data are weighted to be representative of national and regional demographics. This survey dataset can aid researchers, policymakers, businesses, and government agencies in understanding both the extent of behavioral shifts and the likelihood that changes in behaviors will persist after COVID-19.

No abstract