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Register a new userThe Enduring Effects of COVID-19 on Travel Behavior in the United States: A Panel Study on Observed and Expected Changes in Telecommuting, Mode Choice, Online Shopping and Air Travel
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The explosive nature of Covid-19 transmission drastically altered the rhythm of daily life by forcing billions of people to stay at their homes. A critical challenge facing transportation planners is to identify the type and the extent of changes in peoples activity-travel behavior in the post-pandemic world. In this study, we investigated the travel behavior evolution by analyzing a longitudinal two-wave panel survey data conducted in the United States from April 2020 to October 2020 (wave 1) and from November 2020 to May 2021(wave 2). Encompassing nearly 3,000 respondents across different states, we explored pandemic-induced changes and underlying reasons in four major categories of telecommute/telemedicine, commute mode choice, online shopping, and air travel. Upon concrete evidence, our findings substantiate significantly observed and expected changes in habits and preferences. According to results, nearly half of employees anticipate having the alternative to telecommute and among which 71% expect to work from home at least twice a week after the pandemic. In the post-pandemic period, auto and transit commuters are expected to be 9% and 31% less than pre-pandemic, respectively. A considerable rise in hybrid work and grocery/non-grocery online shopping is expected. Moreover, 41% of pre-covid business travelers expect to have fewer flights (after the pandemic) while only 8% anticipate more, compared to the pre-pandemic. Upon our analyses, we discuss a spectrum of policy implications in all mentioned areas.
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During the COVID-19 epidemic, many health professionals started using mass communication on social media to relay critical information and persuade individuals to adopt preventative health behaviors. Our group of clinicians and nurses developed and recorded short video messages to encourage viewers to stay home for the Thanksgiving and Christmas Holidays. We then conducted a two-stage clustered randomized controlled trial in 820 counties (covering 13 States) in the United States of a large-scale Facebook ad campaign disseminating these messages. In the first level of randomization, we randomly divided the counties into two groups: high intensity and low intensity. In the second level, we randomly assigned zip codes to either treatment or control such that 75% of zip codes in high intensity counties received the treatment, while 25% of zip codes in low intensity counties received the treatment. In each treated zip code, we sent the ad to as many Facebook subscribers as possible (11,954,109 users received at least one ad at Thanksgiving and 23,302,290 users received at least one ad at Christmas). The first primary outcome was aggregate holiday travel, measured using mobile phone location data, available at the county level: we find that average distance travelled in high-intensity counties decreased by -0.993 percentage points (95% CI -1.616, -0.371, p-value 0.002) the three days before each holiday. The second primary outcome was COVID-19 infection at the zip-code level: COVID-19 infections recorded in the two-week period starting five days post-holiday declined by 3.5 percent (adjusted 95% CI [-6.2 percent, -0.7 percent], p-value 0.013) in intervention zip codes compared to control zip codes.
It is one of hottest topics in Vietnam whether to construct a High Speed Rail (HSR) system or not in near future. To analyze the impacts of introducing the HSR on the intercity travel behavior, this research develops an integrated intercity demand forecasting model to represent trip generation and frequency, destination choice and travel mode choice behavior. For this purpose, a comprehensive questionnaire survey with both Revealed Preference (RP) information (an inter-city trip diary) and Stated Preference (SP) information was conducted in Hanoi in 2011. In the SP part, not only HSR, but also Low Cost Carrier is included in the choice set, together with other existing inter-city travel modes. To make full use of the advantages of each type of data and to overcome their disadvantages, RP and SP data are combined to describe the destination choice and mode choice behavior, while trip generation and frequency are represented by using the RP data. The model estimation results show the inter-relationship between trip generation and frequency, destination choice and travel mode choice, and confirm that those components should not dealt with separately.
To analyze the influence of introducing the High-Speed Railway (HSR) system on business and non-business travel behavior, this study develops an integrated inter-city travel demand model to represent trip generations, destination choice, and travel mode choice behavior. The accessibility calculated from the RP/SP (Revealed Preference/Stated Preference) combined nested logit model of destination and mode choices is used as an explanatory variable in the trip frequency models. One of the important findings is that additional travel would be induced by introducing HSR. Our simulation analyses also reveal that HSR and conventional airlines will be the main modes for middle distances and long distances, respectively. The development of zones may highly influence the destination choices for business purposes, while prices of HSR and Low-Cost Carriers affect choices for non-business purposes. Finally, the research reveals that people on non-business trips are more sensitive to changes in travel time, travel cost and regional attributes than people on business trips.
Public transit disruption is becoming more common across different transit services, which can have a destructive influence on the resiliency and reliability of the transportation system. Utilizing a recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to unplanned service disruption and disclose the factors that affect their behavior.
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 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. Version 1.0 of the survey contains 8,723 responses that are publicly available. The survey is deployed over multiple waves to the same respondents to monitor how behaviors and attitudes evolve over time. 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 these changes will persist after COVID-19.
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