Evolution of Modal Captivity and Mode Choice Patterns for Commuting Trips: Longitudinal Analysis by Using Cross-Sectional Data Sets
Bibliographic record
Abstract
This paper presents an econometric model that uses multiple repeated cross-sectional datasets to explain temporal evolutions of commuting mode choice preference structures. The model explicitly addresses latent captivity to different modes in addition to systematic elements of choice behaviour. The empirical model is a pooled model and is estimated by pooling three household travel survey datasets together that are collected in the Greater Toronto and Hamilton Area (GTHA) over a 10 year time period. The empirical model clearly explains that there have been significant changes in latent captivity and the mode choice preference structure of commuting mode choice in the GTHA. Changes have occurred in the unexplained component of latent captivity to different modes; in the transportation cost perceptions among different occupation groups, and in the scales of commuting mode choice preferences. Furthermore, the pooled model developed in this paper demonstrates that pooling multiple repeated cross-sectional datasets is a more efficient method of capturing behavioural changes than using a cross-sectional model. Finally, the pooled model reveals that unexplained components of the modal captivities change more over time than the unexplained portions of the systematic utility functions. These findings highlight the necessity of considering latent captivity in commuting mode choice models for proper policy evaluations and forecasting.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".