A database of travel-related behaviors and attitudes before, during, and after COVID-19 in the United States
Rishabh Singh Chauhan, Matthew Wigginton Conway, Denise Capasso da, Silva, Deborah Salon, Ali Shamshiripour, Ehsan Rahimi, Sara Khoeini, Abolfazl, Mohammadian, Sybil Derrible, and Ram Pendyala

TL;DR
This paper presents a comprehensive, longitudinal survey dataset capturing travel behaviors and attitudes in the U.S. before, during, and after COVID-19, enabling analysis of behavioral shifts over time.
Contribution
It provides a detailed methodology and a publicly available dataset of 8,723 responses, capturing diverse travel-related behaviors and attitudes across multiple waves.
Findings
Dataset covers commuting, air travel, online learning, shopping, and risk perception.
Data is weighted to reflect national and regional demographics.
Survey enables analysis of behavioral changes and their persistence post-pandemic.
Abstract
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 Wave 1 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…
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