Comparative Structural Evaluation of Transit Travel Demand using Travel Survey and Smart Card Data for Metropolitan Transit Financing
Bibliographic record
Abstract
Lrge metropolitan areas often comprise multiple municipalities and multiple transit-operating agencies that share infrastructure and passengers. In such cases, financing mechanisms are devised to share costs and revenues among the various jurisdictions. Using Montréal, Canada, as a case study, this paper investigates whether a large sample household travel survey (HTS) can provide sufficiently accurate and detailed information to form the basis for a metropolitan transit financing framework. The evaluation is made possible by the existence of a smart card (SC) fare collection system, deployed across the region, which provides, with some processing, an independent source of transit trip information. The structure of transit travel demand, as measured by SC and the HTS, were compared. The structural elements examined included the types of fare product used, the temporal distribution of trips during a typical weekday, and the spatial and temporal distribution of trips over the multiple networks serving the metropolitan area. The results of the comparison showed that the HTS constitutes a simplified portrayal of transit demand that over-represents symmetrical travel patterns prevalent during peak periods and under-represents other travel patterns. An important consequence of this bias is the over-representation of travel between the suburbs and downtown. Two theoretical allocation scenarios were designed to evaluate the potential effects of these differences on metropolitan-level cost- and revenue-sharing. A simple experiment showed the effects to be significant.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".