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Record W4416684304 · doi:10.1061/jupddm.upeng-5746

Investigating Travel Behavioral Changes throughout 10 Years: A Case Study on Southeast Michigan

2025· article· en· W4416684304 on OpenAlexaff
Fadi Alhomaidat, Taqwa I. Alhadidi, Tamer Eljufout, Mousa Abushattal, Ahmed Jalil Al-Bayati

Bibliographic record

VenueJournal of Urban Planning and Development · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsTravel behaviorTravel surveyTravel timeTRIPS architectureDescriptive statisticsSample (material)Work (physics)Mode choice

Abstract

fetched live from OpenAlex

This paper presents a descriptive analysis of travel behavior over 10 years using household survey data collected by the Southeast Michigan Council of Governments (SEMCOG) in 2005 and 2015, respectively. The data used in this work were 12,000 and 6,500 sample sizes for 2005 and 2015, respectively. Generally, results indicated that a 12% reduction in all trip rates occurred during the study period. On the other hand, trip rates for multiple age ranges, including the elderly, increased from 2005 to 2015. Also, the average travel distance for all modes increased during the study period, and transit was mainly used for long travel distances. Also, several spatiotemporal changes in human travel behavior were analyzed using different travel indicators, namely, travel time, trip purpose, travel mode, and travel distance. The analysis results show the change in travel behavior across different counties in the SEMCOG area during the study period. The study indicates that the travel time changes across different travel modes, as well as trip purposes, were influenced by the economic impact changes during the study period. It was found that travel time distributions for most purposes are concentrated on trips of travel time shorter than 40 min.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.108
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.129
GPT teacher head0.437
Teacher spread0.308 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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