Getting at Mode Share: Comparing 3 Methods of Travel Mode Measurement for School Travel Research
Bibliographic record
Abstract
BACKGROUND: Mode share is an important metric for active school travel programs. Common methods for measuring mode share include Hands Up surveys and family surveys, but these require teacher and parental involvement. We used these methods as part of an evaluation of a school-based bicycle training program, and added a novel observational count approach. This paper compares mode share results across the 3 methods. METHODS: We collected data over 2015-2017 at 16 elementary schools. Our outcome of interest was mode share (walk, drive, and bicycle). RESULTS: We found variations in travel mode estimates between methods and across schools. Overall most school journeys were made by walking (55.7% by observational counts, 46.3% by Hands Up surveys, and 51.5% by family surveys) or car (42.5%, 51.4%, and 46.2%, respectively), and a small proportion by bicycle (1.8%, 2.3%, and 2.2%, respectively). At individual schools, Hands Up and family survey results were similar; there was less agreement between these and observational counts. CONCLUSION: School travel practitioners face pragmatic choices in data collection. Observational counts are a nonintrusive method suited for school-wide travel patterns. Hands up and family surveys may be more appropriate for assessing differences between classrooms, ages, or family characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.115 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".