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Record W1970386109 · doi:10.3141/1776-03

Freeway Quality of Service: What Really Matters to Drivers and Passengers?

2001· article· en· W1970386109 on OpenAlexafffund
Fred L. Hall, Sarah Wakefield, Ahmed Al‐Kaisy

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcMaster UniversityHamilton Health Sciences
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsTRIPS architectureTransport engineeringService (business)Service qualityQuality (philosophy)Level of serviceBusinessPleasureTravel behaviorMarketingEngineeringPsychology

Abstract

fetched live from OpenAlex

Although the concept of level of service for freeways is usually defined in terms of users’ perceptions, very few studies have sought drivers’ or passengers’ views about what is important to them. Such information is particularly important for the evaluation of extended trips on freeways as opposed to trips on a single section or segment. Such information is valuable not only for improving the Highway Capacity Manual but also for establishing appropriate criteria for assessment of intelligent transportation system proposals. The results of focus group sessions are reported in which a group of commuters discussed their views about determinants of the freeway quality of service that they experienced. Total travel time is the most important determinant for them, but a number of other aspects of the trip also matter, including safety, traveler information, and maneuverability (density). The importance of travel time is a reminder that travel is a derived demand, not something that commuters do for the pleasure of the drive.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.130
GPT teacher head0.445
Teacher spread0.315 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations49
Published2001
Admission routes2
Has abstractyes

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