How Far Out of the Way Will We Travel?
Bibliographic record
Abstract
Current travel demand models are calibrated for motorized transportation and do not perform as well for nonmotorized modes. Little evidence exists on how much, and for what reasons, the routes people travel deviate from the shortest-path or least-cost routes generated by transportation models. This paper investigates differences in total distance, road type used, and built environment features for shortest-path routes versus actual routes for utilitarian bicycle trips (n = 50) and car trips (n = 67) in Metro Vancouver, Canada. Bike trips were, on average, 360 m longer than the shortest possible route; car trips were 540 m longer. Regardless of mode, people do not detour far off the shortest route: detour ratios (actual distance/shortest distance) were similar, with three-fourths of trips within 10% of the shortest distance and at least 90% within 25%. Differences in the built environment measures en route suggest why bike commuters chose to detour: the actual routes had significantly more bicycle facilities (traffic-calming features, bike stencils, and signage) than did the shortest-path routes. Compared with shortest-path routes, cyclists spent significantly less of their travel distance along arterial roads and significantly more along local roads, off-street paths, and routes with bike facilities. As expected, car trips were more likely to be along highways and less likely to be along local roads than predicted by the shortest route. The results illustrate factors that might be included in travel models to more accurately model nonmotorized transportation and provide guidance for how dense bike facilities need to be when infrastructure to support cycling is designed.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".