Application of RP/SP Data to the Joint Estimation of Mode Choice Models: Lessons Learned from an Empirical Investigation into Cross-Regional Commuting Trips in the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
This study presents an investigation on the mode choice behaviour of cross-regional commuters in the Greater Toronto and Hamilton Area (GTHA). Cross-regional trips are defined as those crossing the boundaries of two or more municipal or regional jurisdictions, each served by a local transit operator. The GTHA has nine local transit and one regional transit systems with little or no coordination between them. The complexity of cross-regional trips stems from the multimodal nature of long distance travel across multiple regional/local municipalities. Using Revealed Preference (RP) and Stated Preference (SP) data of the Survey of Cross-Regional Intermodal Passenger Travel (SCRIPT), a set of econometric joint RP/SP mode choice models are developed. This paper presents a comparison between a conventional Multinomial Logit (MNL) mode choice model and two models that relax the independent and irrelevant alternative (IIA) assumption, namely Nested Logit (NL) and Parameterized Logit Captivity (PLC) models. The joint RP/SP models reveal meaningful insights into cross-regional commuters’ mode choice behaviour. The developed models now constitute the core of a policy analysis tool, called Interactive Model for Policy Analysis of Cross-Regional Travel (IMPACT), which predicts changes in aggregate modal shares in response to new policies.
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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".