Ex Ante and Ex Post Evaluation of Smart Commute Strategies in the Greater Toronto and Hamilton Area of Canada: Comparison of Aggregate and Disaggregate Approaches
Bibliographic record
Abstract
This study investigated the effectiveness of the Smart Commute program, a well-established travel demand management (TDM) program in the greater Toronto and Hamilton area of Canada. The study exploited a data fusion technique to combine data collected through cross-sectional ex ante and ex post surveys at the workplace of each Smart Commute member. Two types of approaches were used: aggregate statistical analysis and disaggregate choice modeling with the fused-combined data set. The results clarify that aggregate investigation may not always uncover many behavioral details. Aggregate comparisons of the survey data showed that the performance of the Smart Commute program varied by sociodemographic attributes of the employees, including age, employment status, employment shift, and regional municipality of employment. The aggregate investigation also showed that the stated willingness to consider bike, walk, and telework options was not reliable in evaluating the effectiveness of any TDM policy. Complementary to the aggregate investigation, the study used an advanced mixed logit model framework to explore the effects of Smart Commute on commuters’ perceptions about travel attributes. The results of the empirical model revealed more variation in the perceptions of commuters about travel attributes after implementation of TDM interventions. In addition, the perceptions of travel times became more negative after TDM implementation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".