Effect of land use and survey design on trip underreporting in Montreal and Toronto’s regional surveys
Bibliographic record
Abstract
This paper contributes to the literature on travel survey methods by quantifying the relationship between land use, data collection protocol and trip under-reporting in regional travel surveys. While under-reporting more broadly is a recognized problem, the significant increase in underreporting in denser, more urban-type environments identified here has never before been demonstrated or measured. Consequences of this land use-related bias for transportation planning and modelling are explored. The work is carried out by comparing the results of two very similar household travel surveys conducted in 2011 and 2013, in Toronto and Montreal respectively. Using data on over 350,000 persons, a binary logit model for discretionary trip making is estimated and the effects of land use and data collection protocol on under-reporting are isolated. This is done by controlling for mobility tool access, household type and other key determinants of travel demand. Counterintuitive effects for urban type environments found indicate the under-reporting effect is equivalent to a reduction in the pre-existing odds of reporting discretionary trip making in more urban environments of 19 to 29%. When combined with Toronto’s data collection protocol effect, the range increases to 39 to 55%. Results should be of use to transportation planning authorities wishing to make better use of the data collected in large surveys. Recognizing some of the flaws and biases in what is reported, these authorities can complement existing sources of data or modify their approaches to demandbased infrastructure provision to better account for the large number of, largely pedestrian, unreported trips.
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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 | 0.124 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".