Effects of accessibility to the transit stations on intercity travel mode choices in contexts of high speed rail in the Windsor–Quebec corridor in Canada
Bibliographic record
Abstract
Main objective of this paper is investigating the role of transit station accessibility on intercity travel mode choices in contexts of a proposed High Speed Rail. The study area is the Quebec–Windsor corridor, which is the most important corridor in Canada and one of the most important corridors in North America. A web-based joint revealed preference – stated preference survey is used to collect data for empirical investigation. To contribute further to travel survey methods, an innovative social media based data collection approach is taken. As opposed to explicit sample frame-based sample selection approach, it applies a reverse procedure of open sample frame-based data collection. The web-based survey is spread through social media groups (that are open in sense that information of all individuals are not known explicitly) and the collected responses are screened to match with population distributions. Results prove the potential of such data collection approach in extracting representative samples of the population of concern. The collected dataset, which has close representation of the population, is used to estimate discrete mode choice model (Nested Logit model) of intercity mode choices. Empirical model reveals that intercity travellers are more concerned about access to and egress from transit stations than the main in-vehicle travel while selecting intercity travel modes. The result of this investigate imply that transit station accessibility should be given careful consideration for the success of any innovative travel mode, e.g., high speed rail.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".