Integrated Intervening Opportunities Model for Public Transit Trip Generation–Distribution
Bibliographic record
Abstract
An integrated intervening opportunities model (IIOM) was developed for public transit (PT) trips. This model is generation–distribution and supply-dependent, with single constraints only on trip production values for work and study PT trips made during morning peak hours (6:00 to 9:00 a.m.) within the Island of Montreal, Quebec, Canada. Several data sets, including the 2008 origin–destination survey of the Greater Montreal Area, 2006 census of Canada, General Transit Feed Specification network data, and school enrollment data, along with the geographical data of the Greater Montreal Area, were used. The IIOM is a nonlinear model with sociodemographic, socioeconomic, and PT supply characteristics, as well as work and study spatial location attributes. Analysis of the modeling performance by means of several goodness-of-fit measures showed that the IIOM was well behaved (i.e., globally it had good prediction capabilities) and more accurate than the classical gravity model. On the basis of explanatory variables used in the IIOM, the study presents a new tool for PT analysts, planners, and policy makers for studying potential changes in PT trip patterns, as a result of changes in sociodemographic and socioeconomic characteristics, PT supply, and so on. Also, this study opens new opportunities for development of more accurate PT demand models with new emergent data such as smartcard entries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".