Trip-Chaining Trends in the United States: Understanding Travel Behavior for Policy Making
Bibliographic record
Abstract
This paper uses data from the 1995 Nationwide Personal Transportation Survey and the 2001 National Household Travel Survey to examine trip-chaining trends in the United States. The research focuses on trip chaining related to the work trip and contrasts travel characteristics of workers who trip chain with those who do not, including their distance from work, current levels of trip making, and the purposes of stops made within chains. Trends examined include changes in the purpose of stops and in trip-chaining behavior by gender and life cycle. A robust growth in trip chaining occurred between 1995 and 2001, nearly all in the direction of home to work. Men increased their trip chaining more than women, and a large part of the increase was to stop for coffee (the Starbucks effect). It was found that workers who trip chain live farther from their workplaces than workers who do not. It was also found that, in two-parent, two-worker households that drop off children at school, women are far more likely than men to incorporate that trip into their commute and that those trips are highly constrained between 8:00 a.m. and 9:00 a.m. An analysis was done of workers who stopped to shop and those who did not but made a separate shopping trip from home; a large potential to increase trip-chaining behavior in shopping trips was found. Results of these analyses have important policy implications as well as implications for travel demand forecast model development. Finally, this paper uses these analyses to develop conclusions about the utility of transportation policies and programs that use the promotion of trip chaining as a primary travel demand management strategy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".