Enhancing Household Travel Surveys Using Smart Card Data
Bibliographic record
Abstract
Household travel surveys are still one of the most important sources of information used to produce detailed profiles of travel behaviors. They often require a lot of resources and lead to the definition of an average day of travel. This average day integrates behaviors which were probed at different weekdays within the period of investigation. Until very recently, few datasets allowed going beyond such estimation. The implementation of smart card (SC) system changes this situation by making available continuous information on transit use. SC systems output data that can be used to observe transactions on the transit network at any moment, on a continuous basis. While lacking details on the traveler, SC data offer the opportunity to enhance some travel survey estimates. Using data from a small Canadian transit authority, this paper compares various indicators estimated with data from the household travel surveys (2005) and data from the SC fare collection systems for the same time period. This study begins with the identification of transportation objects. Then, several indicators are calculated for comparable elements: date, time, route and fare type. The average weekday that is obtained from the travel survey is compared to each day of operation of the SC system. Results show that there are large variations in transit network use between weekdays. For the most important routes (regular adult card holders), the travel survey matches SC data at 5% level. However, for the origin-destination matrix, SC data is more precise and reveals movements not reported in the survey.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".