Over the past few decades, transportation has been influenced by various factors, including rapid technological advances, changes in demographics, changes in the public’s values and priorities, and ongoing changes in the economy and climate. These forces have shaped the travel choices available, and how Americans travel and allocate time and money to different activities as they navigate their day-to-day lives. To better understand the evolving transportation landscape and changing mobility patterns, this effort leverages data from multiple key national surveys: the American Community Survey (ACS), the American Time Use Survey (ATUS), and the Consumer Expenditure Survey (CE), complemented by other transportation data sources and statistics. The analysis provides a comprehensive overview of mobility trends, focusing on travel metrics such as trip frequency, mode usage, vehicle miles traveled (VMT), commuting patterns, work arrangements, time use across various trip purposes, and household expenditures on transportation. The findings reveal declines in overall trip-making, particularly for commuting and shopping trips, along with a significant increase in telework – which is a pre-COVID trend amplified by the pandemic. While travel patterns have begun to stabilize in the post-pandemic period, some behavioral shifts remain. Overall trip-making has not returned to pre-pandemic levels, and the fraction of individuals reporting zero trips remains considerably higher than in the pre-COVID period. Social and recreational travel is showing signs of recovery, but public transportation use remains subdued, and non-motorized modes like walking and biking have also declined. On the other hand, micromobility options, such as e-bikes and e-scooters, are gaining popularity, and telework continues to influence travel behavior. These trends have important implications for transportation planning in a “new normal” era as the spatio-temporal patterns of travel are considerably different in the post-COVID period. Future infrastructure investments will need to accommodate more flexible and diverse travel patterns, moving beyond a commuting-centric operational model. Transportation planning practices and forecasts that drive policy decisions and investments must be updated using contemporary data that reflects the latest behaviors, choices, and preferences so that future mobility solutions and technologies meet the evolving needs of residents and businesses. This presentation will provide a comprehensive overview of two decades of trends with a discussion of the implications of these trends for policy planning and modeling in the future.

Recruiting respondents from key subregions and population subgroups for travel surveys is increasingly challenging, leading to concerns about data representativeness. Recent U.S. regional household travel surveys (HTS) have tested various strategies to mitigate low and differential nonresponse. This paper evaluates key strategies addressing unit nonresponse, including disproportionate stratification (oversampling), incentives, convenience (multimethod) sampling, and weighting. Using the total survey error paradigm, we propose an analytic framework to assess how these strategies impact representativeness in travel behavior data. We illustrate this approach with data from Metropolitan Council’s 2021–2022 Travel Behavior Inventory (TBI), which covers the Minneapolis–St. Paul region.

First, we review common barriers to evaluating travel survey representativeness. Recent state-of-practice syntheses highlight that self-selection in travel behaviors, beyond just differences in demographic distributions, is a critical yet underexamined concern. However, standard indicators such as response rates and socioeconomic distributions do not evaluate this nonresponse bias directly. Moreover, the unique sample designs and post-survey adjustments of regional travel surveys make it difficult to generalize across studies or identify which strategies improve representativeness. Evaluation becomes even more challenging when multiple approaches are used simultaneously. Finally, there are numerous travel survey behaviors (trip rates, mode choice, etc.) and corresponding statistics (means, proportions, associations, regression coefficients etc.) for which it is critical to have low nonresponse bias, but different MPOs may prioritize evaluating and reducing bias on different behaviors and statistics.

To address these challenges, we develop a framework that:

1. Facilitates evaluation of nonresponse bias on travel behavior measures, rather than just SED representativeness.
2. Can be applied without requiring additional data collection or experimental design changes.
3. Can be adapted to various survey designs and regions.

Then, we use the Metropolitan Council’s TBI data to demonstrate how differential nonresponse introduces bias in socioeconomic distributions and vehicle ownership rates. We then quantify how convenience sampling and weighting adjustments influence both bias and precision in travel behavior estimates. Our findings offer insights into improving survey representativeness and reliability, with implications for future survey designs. We conclude by discussing how this framework can be extended to evaluate other response-enhancement strategies and to improve the connection between survey methods, data quality, and transportation planning decisions.

Household Travel Surveys (HTS) remain a critical tool for understanding travel behavior and informing transportation planning. However, evolving technologies, shifting mobility patterns, and recruitment challenges necessitate continuous adaptation. This study, conducted by Cambridge Systematic (CS) for the Delaware Valley Regional Planning Commission (DVRPC), synthesizes insights from recent HTS efforts across the United States, drawing from survey documentation and interviews with Metropolitan Planning Organizations (MPOs) representing diverse methodological approaches and regional characteristics.

Findings highlight the widespread transition to online surveys and smartphone applications, which have largely replaced traditional paper surveys due to their ability to streamline response collection and validate data in real time. GPS-enabled mobile apps have significantly improved trip reporting accuracy by passively capturing travel data, mitigating recall bias, and reducing respondent burden. While app-based surveys enhance data completeness and quality, disparities in digital access and user preferences necessitate multi-modal survey options, including web-based and phone interviews, to ensure broad participation. Agencies employing app-based methods have observed higher trip counts and more reliable travel diaries, although limitations remain in willingness to participate and interpreting certain activities, such as transit transfers and short stops.

Recruitment strategies remain centered on address-based sampling (ABS) as the standard for ensuring representative participant selection, with some MPOs supplementing ABS with convenience sampling to increase participation among underrepresented groups. Agencies have used social media, community partnerships, and school district collaborations to engage the general and especially hard-to-reach populations. Targeted outreach, bilingual materials, and trusted community organizations have helped improve representation, though response rates remain a challenge. Incentive structures have also evolved, with differential incentives based on response method, household size, and survey duration proving effective in encouraging participation while balancing cost considerations.

In response to changing travel behaviors, many agencies have refined their survey instruments to capture emerging mobility trends, including transportation network companies (TNCs), micromobility, e-commerce deliveries, and hybrid work patterns. Capturing these shifts is essential for accurate demand forecasting and policy development. Additionally, MPOs are increasingly supplementing HTS data with big data sources such as LOCUS, Streetlight, and INRIX to validate travel patterns, analyze visitor trips, and improve model calibration. While these sources provide valuable insights beyond traditional household samples, concerns persist regarding data transparency, biases, and the need for statistical rigor in their integration. Some agencies have taken steps to develop open-source tools to enhance the reliability and equity of data use.

This study provides a comprehensive overview of contemporary HTS methodologies, emphasizing advancements in survey instruments, recruitment strategies, and data integration. The findings offer valuable guidance for agencies seeking to improve survey efficiency, enhance data quality, and ensure equitable representation in future HTS efforts.