Analysis of Stated-Preference and GPS Data for Bicycle Travel Forecasting
Bibliographic record
Abstract
In this paper, we present preliminary results from an ongoing study of cyclists and cycling in the Region of Waterloo, Ontario Canada. The paper describes two data collection efforts. The first is an on-line survey that provides information on cyclists’ demographics as well as their household composition. The survey also gathers data on respondents’ motivation for and obstacles to cycling. The second activity collects data on actual cycling trips using GPS units. We describe these units and the steps taken to validate the data. We use the GPS data to produce trip generation and attraction rates for cycling as a function of land use. We also generate a histogram of observed cycling trip lengths that can be used to calibrate a gravity-type model of trip distribution. We then explore the methods by which the survey and GPS data may be combined to develop multi-class and multi-trip purpose generalized cost functions. These formulations may be applied to prioritizing infrastructure investments, as well as for mode and path choice models. We conclude with a discussion of ongoing research work.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".