Will you ride the train? A combined home-work spatial segmentation approach
Bibliographic record
Abstract
While the influence of land use and transport networks on travel behavior is known, few studies have jointly examined the effects of home and work location characteristics when modelling travel behavior. In this study, a two-step approach is proposed to investigate the combined effect of home and work location characteristics on the intent to use a new public transport service. Using data from the 2019 Montreal Mobility Survey (n=1698), this study examines the intent to use the Réseau Express Métropolitain (REM), a light rail under construction in Montreal, for commuting. A segmentation analysis is first conducted to characterize commuters based on their home and work location characteristics, resulting in six distinct home-work clusters. The clusters are then included in an ordered logistic regression modelling the intent to use the REM, along with socio-economic and attitudinal characteristics. Results from a dominance analysis reveal that the clusters are the third most important determinants of the intent to use the REM, even when controlling for individual characteristics. The addition of the clusters leads to a significant improvement of the model (likelihood of -2388.9 improved from -2400.7, p-value < 0,05). All other clusters have a significantly lower probability (between 32 and 51% less likely) of intent to use the REM than the typical commuters (who commute from the suburbs to downtown, often by transit), at a 95% confidence interval. These findings underscore the implications of pursuing radial public-transport networks, illustrating the ability of the proposed approach to identify which groups are likely to benefit from a public-transport project and to propose recommendations anchored in joint home and work location patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".