The Pulse of the Nation on 3 Revolutions: Annual Investigation of Nationwide Mobility Trends
Bibliographic record
Abstract
This study investigates the disruptive changes brought to transportation by emerging technologies and the COVID-19 pandemic through the analysis of repeated cross-sectional datasets that were collected with multiple survey waves administered in various regions of the United States and Canada. The first data collection was administrated in 2019 through the recruitment of respondents with an online opinion company. As the COVID-19 pandemic started to disrupt the world starting in 2020, two additional rounds of data collection were carried out in Spring 2020 and Fall 2020, to study the disruptions in activity and travel patterns that were caused by the pandemic. Starting in 2020, the data collection was extended to 15 U.S. regions: Los Angeles, Sacramento, San Diego and San Francisco in California; Atlanta, Boston, Chicago, Denver, Detroit, Kansas City, New York, Salt Lake City, Seattle, Tampa and Washington D.C. in other U.S. regions. In addition, the study covered also Toronto and Vancouver in Canada. Several thousands of respondents participated in the various waves of surveys. Some of these respondents were part of the longitudinal component of the dataset, built through inviting previous survey respondents to participate in the new waves of data collection. Additional respondents were recruited using online opinion panels and convenience sampling. The study enabled by the analysis of the data collected with this series of surveys helps understand how mobility patterns are evolving in the country as new technologies disrupt the transportation sector and they evolve from the pre-pandemic to the post-pandemic era. It helps make planning decisions and guide policymaking through an annual data collection that allows us to collect critically-needed information on the evolution of travel patterns and the adoption of new transportation technologies and trends in the selected regions, every year. In this report, the researchers briefly describe the series of data collection and present some summary findings from the analysis of the data collected before and during the COVID-19 pandemic.View the NCST Project Webpage
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".