household travel survey questionnaire

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National Household Travel Survey (NHTS)

New 2022 nextgen nhts core data now available.

Conducted by the Federal Highway Administration since 1969, the National Household Travel Survey (NHTS) is the authoritative source on the travel behavior of the American public. It is the only source of national data that allows one to analyze trends in personal and household travel. It includes daily non-commercial travel by all modes, including characteristics of the people traveling, their household, and their vehicles. FHWA has completed the 2022 NHTS data collection with special focus on equity and those data are now available at:  https://nhts.ornl.gov/

Introduction to the NHTS

The NHTS is a periodic national survey used to assist transportation planners and policy makers who need comprehensive data on travel and transportation patterns in the U.S. The 2022 survey data along with historical data from the 2017, 2009, and 2001 NHTS, and the former Nationwide Personal Transportation Surveys (NPTS) of 1983, 1990, and 1995, are available at the NHTS website:  https://nhts.ornl.gov/ .

The NHTS/NPTS serves as the nation's inventory of daily travel. Data is collected on daily trips taken by households and individuals in those households, over a 24-hour period, and includes:

• purpose of the trip (work, shopping, social, etc.)
• means of transportation (car, walk, bus, subway, etc.)
• travel time of trip
• time of day/day of week

These details are collected for all trips, modes, purposes, trip lengths, and all areas of the country, urban and rural.

Uses of Surveys:

• quantify travel behavior
• analyze changes in travel characteristics over time, and
• study the relationship of demographic and travel over time
• examine the impact of emerging transportation services

For more information, go to the NHTS website .

NHTS Resources

NHTS Products, Publications

NHTS Program Newsletters ( https://nhts.ornl.gov/publications

2022 NHTS Weighted Vehicle Occupancy factors

Moving Forward: The NextGen NHTS

The NextGen NHTS program focuses on providing national travel behavior data on a more frequent basis than prior NHTS efforts. This effort includes moving to a biennial household travel survey and adding annual passive original destination (OD) truck and passenger data products at the national level.

Core Data Program: The NHTS was redesigned to be shorter and more focused. Conducted by Ipsos, the survey will collect data from 7,500 randomly sampled households and 7,500 households that are part of Ipsos’s Knowledge Panel. The survey is currently in field and will capture details on travel behavior from January 2022 through January 2023.

National passive OD data products: The NextGen NHTS also features an OD data component. The OD data contractor (University of Maryland) will be providing national passenger and truck OD data annually for calendar years 2020 through 2024.

To download the 2020 national passenger and truck OD data products or to use a convenient online tool, go to https://nhts.ornl.gov/od . Other online resources include the following:

• Quick start video (.mp4)
• Method document (passenger) (.pdf)
• Method document (truck) (.pdf)

The national passive OD data products summarize travel within and across 583 zones. A map of the zone structure can be found at https://www.fhwa.dot.gov/policyinformation/analysisframework/04.cfm .

NextGen NHTS pooled-fund program

FHWA has established a pooled-fund program to support shared research into travel behavior data as well as provide a contracting mechanism for agencies to purchase additional samples as part of the survey effort and/or more refined geography in the passenger OD data product. For more information, see https://pooledfund.org/Details/Study/651

New NextGen NHTS Origin Destination Data Release

FHWA has released the latest NextGen NHTS data and information. Please visit the Latest NextGen NHTS Data page to learn more. The two datasets are comprised of the traditional NHTS core data associated with why, how, and when people travel and the new comprehensive national multimodal origin- destination estimation (OD) travel data through 583 zones. The OD data includes both between zones (interzonal) and within a single zone (intrazonal) travel. The core data includes both the sample data and weighted data. Both datasets are also supported by online data analysis and visualization tools.

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Please direct all questions and comments to [email protected] .

• CEE.UMD.EDU
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• Director's Message

Next Generation National Household Travel Survey (NHTS): National and Pooled Fund Origin Destination Data

Conducted by the Federal Highway Administration (FHWA), the National Household Travel Survey (NHTS) is the authoritative source on the travel behavior of the American public. It is the only source of national level data that allows researchers to analyze trends in personal and household travel. It includes daily non-commercial travel by all modes as well as traveler characteristics.

FHWA has launched the Next-Generation National Household Travel Survey (NextGen NHTS) to establish a more continuous travel monitoring program with national and local data products. In addition to a probabilistic core travel survey, NextGen NHTS also includes an origin-destination (OD) data program that will produce multimodal passenger and truck travel OD tables at the national and local levels from passively collected data sources. University of Maryland (UMD) has been competitively selected to produce the OD data for the NextGen NHTS program.

National Passenger OD Product Specification

• Time-Period: 2020-2024 full calendar year
• Coverage: All 50 states and District of Columbia
• Zones: FHWA’s specified 582 zones
• Trips Covered: All trips, inter-zonal and intra-zonal
• Modes: Air, Rail, Vehicle, Other
• Purpose: Work, Non-Work
• Distance: (0, 10], (10, 25], (25, 50], (50, 75], (75, 100], (100, 150], (150, 300], >300 miles
• Seasonality: Annual OD in 2020; Monthly OD in 2021~2024

National Truck OD Product Specification

• Seasonality: Annual OD in 2020~2024

National OD Zone Structure:

For national level data, the FHWA has developed 582 zones. Of the 582 zones,446 are derived from the original 389 OMB defined metropolitan statistical areas (MSAs) and 136 are newly created zones by the FHWA from rural or Micropolitan Statistical areas.

University of Maryland Team Strengths:

• High quality products using extensive data, rigorous computation and validation methods;
• Robust and sustainable data sources for passenger and truck travel;
• Transparency in raw data, computation methods, and final data product validation;
• Proven track record in delivering national OD data to FHWA;
• Experience in delivering passive data products to agencies in all 50 states and D.C.; and
• Expertise with the NHTS program and prior NHTS surveys.

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National Household Travel Survey

The 2001 National Household Travel Survey (NHTS) was a joint effort undertaken by the Bureau of Transportation Statistics (BTS) and the Federal Highway Administration (FHWA) to collect data on the volumes and patterns of passenger transportation. The 2001 survey collected detailed information on both the long-distance and local travel by the American public. This included items such as the mode(s) of transportation, purpose of the trip, duration and distance, and accompanying household members. In addition, it also captured the demographic, geographic, and economic data for analysis purposes.

>  2017 NHTS conducted by the FHWA

3.3 Million Americans are "Stretch Commuters" Traveling at Least 50 Miles One-Way to Work

• America on the Go: Long Distance Mode Choice, 2001-2002, released May 2006
• National Household Travel Survey: Pre- and Post-9/11 Data Documentation
• Frequently Asked Questions
• Winter Travel Quick Facts
• Summer Travel Quick Facts
• Daily Travel Quick Facts
• Long Distance Business Travel Quick Facts
• Long Distance Travel Quick Facts
• "Stretch Commute" Quick Facts
• Highlights of the 2001 National Household Travel Survey
• America on the Go: U.S. Holiday Travel - November 2003
• America on the Go: U.S. Business Travel - October 2003
• 2009 NHTS Datasets
• NHTS Survey: Pre- and Post-9/11 Data Documentation
• NHTS—National Data and Analysis Tool CD
• Local Area Transportation Characteristics for Households (NHTS Transferability)
• Nationwide Personal Transportation Transportation Survey (1995, 1990)
• American Travel Survey (1995)

Federal Highway Administration - NHTS 2009

Household Travel Survey Program

The regional travel survey collects day-to-day information from households in the central Puget Sound region: how we travel, where we go, how long it takes us - even where we choose to live and whether we get home deliveries.

The surveys help the Puget Sound Regional Council and communities throughout the region plan for transportation improvements, new housing, and job growth. For example, what role can transit, biking and walking play in improving regional mobility? Having a complete picture of activity and travel patterns helps produce better plans for the future.

The spring 2021 household travel survey was part of a six-year program with three waves (two-year survey cycles). The 2021 effort builds upon the 2019 and 2017 household travel surveys to continue to support a wide variety of agency and member data and analysis needs. 

The next wave of the travel survey program will be in 2023.

Explore the Survey

Learn more about the survey with the Introduction to Household Travel Survey Guide  (PDF).

Household Travel Survey Data Explorer App

Spring 2017-2019-2021 Travel Survey Program

PSRC Data Portal: Household Travel Survey

•   Regional Travel Survey 2021 Final Report  (PDF)
•   Regional Travel Survey 2021 Recommendations Report  (PDF)
•   Regional Travel Survey 2021 Codebook  (XLSX)
•   Regional Travel Survey 2021 Questionnaire  (PDF)
•   Regional Travel Survey 2019 Public Release 2 Codebook  (ZIP)
•   Regional Travel Survey 2019 Technical Report  (PDF)
•   Regional Travel Survey 2019 Appendix A: Questionnaires  (ZIP)
•   Regional Travel Survey 2019 Appendix B: Invitational Materials  (ZIP)
•   Regional Travel Survey 2019 Technical Note on Categorization of Households by Race/Ethnicity  (PDF)
•   Regional Travel Survey 2017 Read Me Document  (PDF)
•   Regional Travel Survey 2017 Erratum Note About Data Files - Feb. 27, 2019  (PDF)
•   Regional Travel Survey 2017 Public Release 2 Codebook - updated Feb. 27, 2019  (XLSX)
•   Regional Travel Survey 2017 Technical Report  (PDF)
•   Regional Travel Survey 2017 Appendix 1: Questionnaire  (ZIP)
•   Regional Travel Survey 2017 Appendix 2: Invitation  (ZIP)
•   2021: Travel in Regional Growth Centers  (PDF)
•   2021: Trends in Deliveries  (PDF)
•   2021: Trends in Telework  (PDF)
•   2021: Women's Travel Needs  (PDF)
•   2021: Trends in Walking and Biking  (PDF)
•   2019: Cost of Housing Top Reason for Displacement  (PDF)
•   2019: Who's Affected by Housing Displacement?  (PDF)
•   2019: People of Color More Likely to Use Transit with Improvements  (PDF)
•   2019: Use of Ride-Hail and Car-Share Services  (PDF)
•   2017: Autonomous Vehicles  (PDF)
•   2017: Employer Benefits, Parking and Transit  (PDF)
•   2017: How People Choose Their Homes  (PDF)
•   2017: Mode Choice  (PDF)
•   2017: How Do Older Adults Get Around?  (PDF)
•  2017: Who Walks and Bikes in the Region?  (PDF)

2015 Travel Survey Program

• 2015 Household Travel Survey  (ZIP)
• ReadMe Document  (PDF)
• Codebook  (XLSX)
• Dataset Guide  (PDF)
• Technical Memo  (PDF)
• Appendix 1 - Questionnaire  (PDF)
• Appendix 2 - Survey Screenshots  (PDF)
• Appendix 3 - Print Materials  (PDF)
• Appendix 4 - Reminder E-Mails  (PDF)

2014 Travel Survey Program

• 2014 Household Travel Survey  (ZIP)
• Trip Linking Notes  (PDF)
• Weighting Process Summary  (PDF)
• Weighting Process  (PDF)
• Survey Instrument  (PDF)

Earlier Travel Surveys

PSRC has been conducting travel surveys for decades. Please contact Brian Lee for  information about the following surveys:

• 1989-2002 Transportation Panel Survey
• 1999 Travel Survey
• 2006 Travel Survey

National Household Travel Survey 2020

South Africa , 2020

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ZAF_2020_NHTS_v01_M

Other Household Survey [hh/oth]

Sample survey data [ssd]

Households and individuals

Version 01: Edited, anonymized dataset for public distribution.

Household: household characteristics, socio-economic circumstances of households. Individual: demographic information (sex, age, race), domestic transport and tourism travel patterns, and attitudes concerning transport

National coverage

The lowest level of geographic aggregation covered by the data is District Municipality.

The target population of the survey consists of all private households in all nine provinces of South Africa and residents in workers' hostels. The survey does not cover other collective living quarters such as students' hostels, old-age homes, hospitals, prisons and military barracks.

The sample design for the NHTS was based on a master sample (MS) that used a two-stage, stratified design with probability-proportional-to-size (PPS) sampling of PSUs from within strata, and systematic sampling of dwelling units (DUs) from the sampled primary sampling units (PSUs). A self-weighting design at provincial level was used and MS stratification was divided into two levels, primary and secondary stratification. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarized at PSU level. The following variables were used for secondary stratification, household size, education, occupancy status, gender, industry and income.

Census enumeration areas (EAs) as delineated for Census 2001 formed the basis of the PSUs. The following additional rules were used: • Where possible, PSU sizes were kept between 100 and 500 dwelling units (DUs); • EAs with fewer than 25 DUs were excluded. • EAs with between 26 and 99 DUs were pooled to form larger PSUs and the criteria used was same settlement type; • Virtual splits were applied to large PSUs: 500 to 999 splits into two; 1000 to 1499 splits into three; and 1500 plus split into four PSUs; and • Informal PSUs were segmented.

A Randomized Probability Proportional to Size (RPPS) systematic sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3080 PSUs were selected. In each selected PSU a systematic sample of dwelling units was drawn. The number of DUs selected per PSU varies from PSU to PSU and depends on the Inverse Sampling Ratios (ISR) of each PSU.

The survey questionnaire consisted of 9 sections: Section 1: General health and functioning, social grants and social relief Section 2: General travel patterns Section 3: Education and education-related travel patterns Section 4: Work-related travel patterns (age 15 years and above) Section 5: Business trips Section 6: Other travel patterns Section 7: General household information Section 8: Household attitudes and perceptions about transport Section 9: Survey officer questions

• Face-to-face [f2f]

Public access data, available to all

Statistics South Africa. National Household Travel Survey 2020 [dataset]. Version 1. Pretoria: Statistics South Africa [producer], 2021. Cape Town: DataFirst [distributor], 2021. DOI: https://doi.org/10.25828/NRVJ-1663

The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.

Copyright, Statistics South Africa

DDI_ZAF_2020_NHTS_v01_M

Metadata version

Version 01: This metadata was downloaded from the DataFirst website ( https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/central ). The following two metadata fields were edited - Document and Survey ID.

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• Travel Surveys

Travel surveys provide information on behavior characteristics that are essential for the development of travel demand models and in the transportation planning process. The surveys are used to gather information suitable for gaining an in-depth understanding of travel behavior in the region, including individuals within households and regional establishments, their travel activities, demographics and other factors that affect travel in the New York metropolitan area. Travel survey activity also takes place nationally. Generally, New York Metropolitan Transportation Council (NYMTC) has utilized a decennially deployed Regional Household Travel Survey (RHTS) for use in the development of its New York Best Practices Model (NYBPM), an activity-based travel demand model. This process is rather costly to deploy and process. In preparation of its next RHTS, NYMTC is researching and evaluating opportunities to enhance and supplement the RHTS.

Smartphone Survey Pilot Study (2021)

September 11 Scholar Research Report – The Future of Travel Surveys (2021)

Innovative Data Collection Recommendations (2016)

Regional Establishment Survey (RES) (2015/16)

Regional Household Travel Survey (RHTS) (2010/2011)

NYC Mobility Report

National Household Travel Survey (NHTS)

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• Published: 23 September 2021

A database of travel-related behaviors and attitudes before, during, and after COVID-19 in the United States

• Rishabh Singh Chauhan   ORCID: orcid.org/0000-0001-7188-557X 1 ,
• Matthew Wigginton Bhagat-Conway   ORCID: orcid.org/0000-0002-1210-2982 2 ,
• Denise Capasso da Silva   ORCID: orcid.org/0000-0003-1414-8439 3 ,
• Deborah Salon   ORCID: orcid.org/0000-0002-2240-8408 2 ,
• Ali Shamshiripour 1 ,
• Ehsan Rahimi   ORCID: orcid.org/0000-0002-8649-7542 1 ,
• Sara Khoeini 3 ,
• Abolfazl (Kouros) Mohammadian   ORCID: orcid.org/0000-0003-3595-3664 1 ,
• Sybil Derrible   ORCID: orcid.org/0000-0002-2939-6016 1 &
• Ram Pendyala 3  

Scientific Data volume  8 , Article number:  245 ( 2021 ) Cite this article

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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.

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15141945

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Background & summary.

The COVID-19 pandemic has spread across the world, infecting tens of millions and killing over one million people 1 . By March 2021, the United States (U.S.) had recorded the highest number of confirmed COVID-19 cases and COVID-19 related deaths in the world 1 . Since social distancing is one of the most effective measures in containing the spread of the infection 2 , several U.S. states issued various restrictions including stay at home orders. Moreover, numerous restaurants and bars closed for dine-in services, various recreation facilities were shut down, many offices and schools switched from meeting in-person to meeting online, and travel restrictions were imposed. These measures had a profound impact on how people in the U.S. went about their daily lives.

To understand the current and future impacts of the pandemic, we conducted a nationwide online survey. The goal of the survey is to capture attitudes and shifts in travel-related choices of people across the nation both during the pandemic and once COVID-19 is no longer a threat. The data are shared publicly in order to help government agencies and businesses prepare for the future. We are conducting additional survey waves with the same respondents to monitor how people’s choices evolve over the course of the pandemic and beyond.

An early version of the survey took place from April to June 2020, when the stay at home orders were in place in most parts of the country 3 , 4 ; this portion of the data collection is referenced as Wave 1 A . A slightly-modified larger-scale survey, Wave 1B ,was deployed between late June and October 2020. Subsequent survey waves are being conducted as the situation evolves. The collected data are released as they become available and necessary procedures for cleaning, documenting, and weighting the data are completed. This procedures for data processing are detailed in this paper. The present article focuses on data from the first wave of the survey.

In the months following the beginning of the spread of COVID-19, several efforts have been made to collect data related to COVID-19. In fact, many datasets have been compiled, specifically on COVID-19 testing 5 , medical imaging of COVID-19 cases 6 , the timeline of government interventions 7 , policy announcements 8 , implementation and relaxation of public health and social measures 9 , epidemiological data 10 , mobility-related data 11 , and out-of-home activity information 12 , to name a few. Researchers also turned to social media platforms, like Twitter and Instagram, to gather COVID-19-related data 13 , 14 , 15 , 16 . Furthermore, several surveys have been conducted to measure the impacts of the pandemic 17 , 18 , 19 , some of which are now released for public use 20 , 21 .

Our survey data are different from most others in several ways. First, it is comprehensive insofar as it includes data about a wide range of topics including commuting, daily travel, air travel, working from home, online learning, shopping, attitudes, risk perception, and socioeconomic and demographic details. Second, it captures detailed information about behaviors before and during the COVID-19 pandemic, as well as the choices that people expect to make when the COVID-19 virus is no longer a threat. Third, it was collected from respondents across the U.S., covering diverse socio-economic backgrounds, professions, education levels, and ages. Fourth, the survey is a true longitudinal panel survey, collecting data in multiple waves from the same individuals at regular intervals. Finally, the data are made publicly available to promote data-driven analysis and research.

The next section describes the data collection methodology, the questions included in the survey, the survey deployment process, and the participant recruitment strategy. Next, the data records section describes the data file types and metadata. Subsequently, the technical validation section explains the procedure for the survey data cleaning and weighting. Lastly, the final section provides additional notes for data users.

Ethical compliance

Our study protocol was approved by both Arizona State University (ASU) and University of Illinois at Chicago (UIC) Institutional Review Board offices. Participants were informed that their participation is voluntary, and that their responses are shared anonymously. An online informed consent was obtained from everyone who responded to the survey.

Survey questions

The data were collected through an extensive online survey with over 120 questions. The survey questions can be broadly divided into three categories: (1) retrospective questions focusing on the period before COVID-19, (2) questions about the period during COVID-19, and (3) prospective questions on respondent expectations for a future period in which COVID-19 is no longer a threat. The questions cover a wide variety of subjects including commuting habits, discretionary travel choices, work-related questions, study-related questions, shopping, dining, and so on – all before, during, and expected after the pandemic.

The survey questions can be classified into eight categories based on question subject type, namely: demographics, work, study, shopping and dining, transportation, and general attitudes. Table  1 describes each of these categories.

Survey recruitment

From April to mid-June 2020, initial Wave 1A responses were collected from a convenience sample via mailing lists, social media outreach, and mainstream media articles. A total of 1,110 responses were collected during this phase.

From late June onward, Wave 1B, the modified version of the survey, was deployed through survey invitations sent to a random email list purchased from a data marketing company. The list contained 350,000 email addresses belonging to people in 24 metropolitan areas across the U.S., as well as the state of Ohio (see Fig.  1 ). We purchased 100,000 additional email addresses of people randomly selected from across the country, including rural areas and excluding the areas covered by the first 350,000 emails. A total of 1,116 responses were received from the email list. Unfortunately, major email service providers quickly began marking our survey invitations as spam, while some smaller providers did not. While we took several steps to mitigate this issue, including changing the wording of the emails, changing the source of the emails (a uic.edu, asu.edu, or covidfuture.org email address), we were ultimately not able to fully solve this problem and saw a lower response rate from individuals with addresses from major email providers.

Distribution of survey respondents by the state of residence for survey dataset version 1.0. Alaska and Hawai’i are in the same weighting division as California, Oregon, and Washington.

Survey invitation emails were also sent to an additional list of approximately 39,000 email addresses from the Phoenix metropolitan area purchased for a previous survey effort 22 . This list yielded 782 responses. The survey invitation emails were sent using Amazon Web Services (AWS) and through the Qualtrics platform. Every 20 th respondent who was invited through the purchased email addresses received a $10 incentive as a gift card. Respondents also had the option to donate their survey incentive to a charity. Invitees received two reminders as part of efforts to maximize response rates.

An additional 5,250 responses to the Wave 1B survey were collected through a Qualtrics Online Panel. Qualtrics recruits these respondents from a variety of panels maintained by other firms and uses quota sampling to recruit respondents that are demographically representative of the nation. The Qualtrics quotas were set to collect information from 20 U.S. metropolitan areas, mostly consistent with the metropolitan areas sampled from the purchased email list, as well as the states of Ohio, Utah, North Carolina, upstate New York, and rural areas. In order to obtain samples that would represent the population in each of the selected geographies, quotas were imposed in the Qualtrics online panel subsample to guarantee representation based on income, age, race and ethnicity, and education. We requested all respondents to provide their email addresses in order to recontact them for subsequent survey waves. Since the Qualtrics respondents are professional survey takers, we designated most questions as mandatory, and we included attention check questions, which are shown to improve response quality 23 .

The distribution of responses by geography, as well as the targeted metropolitan areas, are shown in Fig.  1 . Figure  2 shows the distribution of responses by recruitment method, available in the “org” variable in the dataset. The geographical targets were chosen based on geographic and metropolitan area size diversity, as well as the state of the virus spread in May 2020.

Distribution of Record by Source (from the survey dataset version 1.0).

Figure  1 shows the distribution of survey respondents across the U.S. (50 states and the District of Columbia). Following our recruitment strategy, a greater number of responses come from larger and more urban states. Arizona is overrepresented due to the oversample of Arizona respondents in the email-based deployment. The respondents from the initial Wave 1A sample are also more likely to hail from Arizona as the Arizona State University survey team’s network is heavily Arizona-based. When the data are weighted, any geographic discrepancies at the census division level are controlled and overrepresentation of Arizona is controlled/corrected separately.

Additional survey waves

To monitor how people’s attitudes and behaviors evolve, survey respondents are contacted again with at least two shorter follow-up surveys, approximately four months apart in spring and fall 2021.

Data Records

The survey dataset 24 can be accessed from the ASU Dataverse at: https://doi.org/10.48349/ASU/QO7BTC . The dataset is available in CSV (comma-separated value) format. Since the data will be updated periodically, the data are versioned—in this article, results from the survey dataset version 1.0 are reported. The dataverse also contains the database codebook containing the metadata and explaining the variables. The codebook contains a changelog for each new version.

The respondents to Waves 1A and 1B received similar but not identical surveys. We have merged the responses to these two versions of the survey into the final dataset wherever possible. For some variables, the questions were identical, whereas for other variables, harmonization of similar responses was required. In the dataset, variables ending in ‘_harm’ are harmonized between the two datasets, variables ending in ‘_w1a’ are available only for Wave 1A respondents, variables ending in ‘_w1b’ are available only for respondents from our Qualtrics Online Panel, purchased email lists, and anyone who found the survey via the COVIDFuture web site or email lists after June 19, 2020 (start date of Wave 1B). Variables with no suffix were asked the same way between the two surveys, and no harmonization was necessary. We also provide a file containing only Wave 1B responses and variables, which simplifies analysis of the Wave 1B data.

Technical Validation

Data cleaning.

To monitor respondents’ attention to survey questions in the Qualtrics online panel, attention check questions were included. Respondents were allowed to miss one attention check and be given an opportunity to answer that section again. If they missed an attention check twice, or both attention checks once, their survey was terminated.

We additionally undertook several quality checks to help ensure that the collected data were valid. We removed any respondents who reported that they shop for groceries both in-store and online every day, or expect to after the pandemic, as these are likely to be invalid responses. We also removed respondents who reported strongly agreeing or strongly disagreeing with all COVID-related attitudes, as some of these were worded positively and some negatively. Several additional quality checks were undertaken in the Qualtrics Online Panel as part of Qualtrics’ data cleaning process, including a check for people finishing the survey too quickly.

Respondents that did not report a state of residence, reported living outside the 50 states and the District of Columbia, or did not provide answers to all of the control variables used in the data weighting process described in the next section were removed from the data. Due to this restriction, 558 records with missing control variable information, 59 records with missing home location, and one response from Puerto Rico were not included in the final dataset encompassing responses received through October 14, 2020. Further steps in data preparation will include imputation of missing data, which will allow for some of these omitted records to be recovered in the next version of the dataset. Among the respondents who were not included in the dataset due to missing control variable information, there are 34 respondents who declared their gender as Other; these respondents could not be included because the Census offers no control marginals to weight these records. Further data weighting procedures will attempt to incorporate non-binary gendered individuals on the dataset. Due to the data cleaning and filtering process applied to responses obtained through October 14, 2020, a total of 618 records were not included in the published dataset.

Data weighting

Because the raw data are not fully representative of the U.S. population, weights were calculated using the following control variables: age, education, gender, Hispanic status, household income, presence of children, and number of household vehicles. The weighting procedure accounts for the true population characteristics at the person level. Household-level variables (i.e., income, presence of children, and number of vehicles) were controlled at the person level as well. For example, the marginal distribution used for presence of children refers to the share of adults aged 18 years and older living in household with children, instead of the share of households that have children as it is usually represented. Those marginal distributions were computed using data from the Integrated Public Use Microdata Sample and the American Community Survey (ACS) 2018 1-year data 25 using the sample 18 and older in each of the weighting region boundaries. A noteworthy consequence of this approach is that adjusted household weights are necessary to evaluate household-level characteristics since individuals from larger households are more likely to be represented in the survey (given there are more individuals in these households), and thus have a higher probability of being selected. Weights for household-level analysis can be computed by dividing the person-level weight (provided in the data) by the number of adults in the household.

The national sample was divided into nine regions based on the reported home state (Table  2 ). Each region’s sample was then weighted to match the distributions observed in ACS 2018 1-year estimates 25 , meaning that the survey is demographically representative at the level of each region as well as the entire U.S. The unweighted and weighted survey results are shown in Table  3 ; the weighted results closely replicate population distributions, with inevitable minor deviations on variables that were not controlled in the weighting process.

Weights were calculated using iterative proportional fitting (IPF) procedures embedded within the synthetic population generator PopGen2.0 26 , 27 , 28 . Univariate marginal control distributions were derived from the Integrated Public Use Microdata Sample, American Community Survey (ACS) 2018 1-year data 25 .

Usage Notes

Since the survey will be followed by at least two follow-up survey waves, the database will be updated periodically after the data for each wave is collected, cleaned, and weighted. Each version of the data will be uploaded to the ASU Dataverse and assigned a new DOI number, and all previous versions will remain available to promote reproducibility.

The weights were developed to produce a sample that is representative of the U.S. population, as well as representative of nine divisions within the U.S.: eight census regions (with East and West South Central combined due to small samples in these regions), and a separate category for Arizona due to its large number of respondents. The weights are not guaranteed to produce a representative sample for other (smaller) geographies. When evaluating subsamples at a finer geography (e.g., state or metropolitan area), data users should compare marginal distributions of key demographic variables with the census, and re-weight the data if needed to be representative of the area being analyzed.

Some questions differ between Waves 1A and 1B. Therefore, we have weighted the dataset twice: once including all respondents (Waves 1A and 1B), and once excluding respondents to the Wave 1A sample. Data users should use the Wave 1B weights whenever using variables that are not present in the convenience sample. Since Wave 1A data deviates significantly in terms of population representativeness 4 , there are no weights for questions asked only of Wave 1A respondents. In the file with only Wave 1B responses, only Wave 1B weights are presented.

This unique dataset provides insights on attitudes and behaviors not just before and during pandemic, but also on what might be expected after the pandemic. Possible use cases include modeling of during-pandemic and longer-term changes in mode use, air travel, transit ridership, work from home, and traffic congestion (especially for peak period traffic planning). Published uses of this dataset are documented in Capasso da Silva et al . 29 , Chauhan et al . 30 , Mirtich et al . 31 , and Salon et al . 32 .

Code availability

No codes were developed for this research.

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Acknowledgements

This research was supported in part by the National Science Foundation (NSF) RAPID program under grants no. 2030156 and 2029962 and by the Center for Teaching Old Models New Tricks (TOMNET), a University Transportation Center sponsored by the U.S. Department of Transportation through grant no. 69A3551747116, as well as by the Knowledge Exchange for Resilience at Arizona State University. This COVID-19 Working Group effort was also supported by the NSF-funded Social Science Extreme Events Research (SSEER) network and the CONVERGE facility at the Natural Hazards Center at the University of Colorado Boulder (NSF Award #1841338) and the NSF CAREER award under grant no. 155173. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders.

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Rishabh Singh Chauhan, Ali Shamshiripour, Ehsan Rahimi, Abolfazl (Kouros) Mohammadian & Sybil Derrible

School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA

Matthew Wigginton Bhagat-Conway & Deborah Salon

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Contributions

R.P., A.M., S.D., D.S. and S.K. planned the project. D.S., M.C., D.C.S., R.C., E.R. and A.M. prepared the survey questions. M.C., D.C.S. and D.S. designed the survey flow logic. R.C., D.C.S., M.C., D.S. and S.D. deployed the survey. M.C. and D.C.S. performed data cleaning and survey data analysis. D.C.S. weighted the dataset. M.C. and D.S. worked on sending out the incentives to the selected respondents. R.C. prepared the first draft. All the authors made significant contributions to manuscript editing and approving the final version of the manuscript.

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Correspondence to Rishabh Singh Chauhan .

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Chauhan, R.S., Bhagat-Conway, M.W., Capasso da Silva, D. et al. A database of travel-related behaviors and attitudes before, during, and after COVID-19 in the United States. Sci Data 8 , 245 (2021). https://doi.org/10.1038/s41597-021-01020-8

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Travel surveys

Generic Survey Process

Survey Planning

Survey Design

Field Implementation

Data Preparation

Data Analysis

Current Concerns

Recent Methods

Gamified Web-Based Surveys

Page categories

Topic Circles

Travel Surveys

More pages in this category:

Travel surveys have been conducted for over 50 years. At the beginning of the 1950s the field of transportation planning had begun evolving into the regional type of planning performed today, and it was quickly realized that roadside surveys alone were not sufficient for regional planning purposes. Hence, the household travel survey was developed and quickly became industry standard. In the early days of household travel surveys, large data sets were collected using face-to-face home interviews with sample sizes that were often as big as 1–3% of the population.

The most common types of travel surveys include:

Household travel surveys which are used to track the travel behavior and/or activities of households, generally employing journal methods,

External surveys which are used to understand vehicle travelers entering or leaving a study area, or crossing key screenlines within a study area,

Transit onboard surveys which are used to track transit passengers as they travel,

Commercial vehicle surveys which are surveys of taxi and truck owners, operators, or dispatchers that are designed to track commercial vehicle travel,

Establishment surveys which are taken at places of employment to develop trip attraction measures,

Visitor surveys which are specialized surveys designed to describe travel by visitors, and travel to and from special trip generators such as airports,

Parking surveys which are used to understand parking behavior in specific locations or parking lots,

Stated-preference surveys which are used to ask questions about travel preferences, and

Gamified web-based surveys can be used in special circumstances.

# Generic Survey Process

Backstrom and Hursh-Cesar (1981) [1] divided the generic survey implementation process into the 20 steps. These 20 steps can be classified into five general stages: (1) survey planning, (2) survey design, (3) field implementation, (4) data preparation, and (5) data analysis.

# Survey Planning

# survey design, # field implementation, # data preparation, # data analysis, # current concerns.

Sample sizes have dropped considerably in the last few decades due to rising costs with lower response rates. Surveys are now often in the range of 2500–10,000 households, which usually represents significantly less than 1% of households in the region. Because funding for travel surveys is decreasing as government budgets become tighter, travel data are also being collected less frequently.

# Recent Methods

Several methods have been adopted to augment the traditional, diary-based household travel surveys. Some of these methods include GPS-enabled surveys and longitudinal surveys. See the paper "Household travel surveys: Where are we going?" [2] for an overview. In this paper, Stopher and Greaves (2007) talk about GPS-enabled surveys and longitudinal surveys. They also review a study which tests the use of synthetic_travel_data, which was constructed using census data and a national household travel survey.

# Gamified Web-Based Surveys

Why a gamified survey? Gamified web-based surveys are becoming more common in market research to encourage response. The main problem we face with online research is motivating people to take part in surveys and give us their full attention during the time they are completing them. The key reason for this problem is simply that surveys are generally seen as being boring.

A ‘gamified’ web-based surveys is useful when there already is information about the population of interest: this kind of survey will not give you representative data about a full population. Respondents are typically younger, male, and interested in the topic. It helps to have a larger population-based sample to help with population estimation and put the results of the gamified stated responses into context.

Gamified approaches are considered when the in the following cases::

• The target sample was a “hard to reach" population
• Need to increase interest and engagement (perhaps previous surveys had a lot of drop-outs)
• There are quite a few options or paths for the respondent to consider. A gamified approach allows us to experiment with scenarios and barriers to link behavior in the game with stated motivations (such as in stated preference or stated response surveys)

Attributes of the ‘gamified’ survey:

• Alternatives presented graphically with animation and a bit of humor
• Set up as a quest or a mission
• Respondents had to identify and play out a scenario
• “Beat the clock” competition and prizes

The game should be designed in detail before any graphics are developed. Every question, the complete questionnaire ‘flow’, and all response categories should be decided upon before graphic development. Other cautions:

Be careful of ‘Tone’: what may be edgy to one person is off-putting to another

Don’t give too much information: people can only absorb so much in a single screen

Looking at secondary choices when typical behavior is blocked is an important data element. But, don’t frustrate the players too much with barriers and blocks; every page should have a ‘skip’ option.

Keep it short

Ask for people motivations and then give them choices to show how they would behave (or visa-versa): getting the ‘stated reason’ and the choice-path in the game are both valuable and together very powerful.

Before adding a lot of demographic information to the survey, look and see what Google analytics can provide.

It is possible to sample in a specific geography by having people enter zip code or other location data to play.

Beware of web-bots: many many will try to get into your portal and crash the site. It is useful to start a web-based survey/game with a required response to weed out web-bots.

“Scalability” across many browsers and devices is currently a challenge, but expect HTTP/2 to help. Plan to spend resources making sure the web survey is equally responsive on different devices and through different browsers. A full pre-test is valuable and Google analytics also has some helpful information.

Remember: On-line surveys in general are not probability samples, there is bias from self-selection.

The gamified approach has not been tested enough to say that the data from a game approach is more reliable than data obtained from traditional methods. There is still “non-commitment” bias—the fact that respondents are playing a game and not making choices in the real world. People can say they would use an alternative without having to actually commit themselves to the inconvenience and uncertainty of shifting to a new mode or route. In actual situations, people may find it more difficult to change their habitual travel patterns.

However, the little research that has been done shows there was very little item non-response, quite a bit of ‘sharing’ on social media, and respondents wanting to play more than once (you have to decide whether to allow multiple plays and how to code them).

Overall, the testing of gamified approaches shows the data were coherent and the results were useful.

# References

The Online Travel Survey Manual provides a comprehensive overview of travel surveys. It is curated by Transportation Research Board’s Travel Survey Methods Committee (ABJ40).

Backstrom, Charles and Gerald Hursh-Cesar. (1981) Survey Research, 2nd edition, John Wiley & Sons. ↩︎

Stopher, P. R., & Greaves, S. P. (2007). Household travel surveys: Where are we going? Transportation Research Part A: Policy and Practice, 41(5), 367-381. Chicago. ↩︎

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