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Record W2145546752 · doi:10.3141/1917-22

Trip-Chaining Trends in the United States: Understanding Travel Behavior for Policy Making

2005· article· en· W2145546752 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsChainingTRIPS architectureWork (physics)Travel behaviorTrip generationTransport engineeringBusinessTravel surveyDemographic economicsGeographyAdvertisingMarketingEconomicsEngineeringPsychology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.260
GPT teacher head0.482
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it