Examining Travel Distances by Walking and Cycling, Montréal, Canada
Bibliographic record
Abstract
Active transportation – especially walking and cycling – is undergoing a surge in popularity in urban planning and transportation circles as a solution to the environmental and congestion issues plaguing many cities. However, in order to promote active transportation as a realistic alternative, it is necessary to better understand the distances that pedestrians and cyclists are willing to travel for different purposes. Better understanding of these distances will allow for transportation planners, designers, developers, and decision-makers to know how close destinations need to be placed to promote the use of non-motorized modes of transport. This paper focuses on how far people are willing to walk or cycle to different destinations in Montreal, Quebec, Canada. Also, it examines how travel distances vary within various geographic areas and by individuals’ travel and socio-economic characteristics. This research uses the 2003 Montreal Origin-Destination Survey (O-D Survey) to calculate the network distance traveled by pedestrians and cyclists and to obtain travel and socio-economic characteristics for each individual. Primarily, the paper reveals that median walking distance recorded in the O-D survey is greater than the commonly-accepted distance of 400 meters (1/4 mile). The median walking distance documented in this study is approximately 650 meters and higher for work purposes (800 meters). While no widely-held standard exists for cycling, the analysis reveals a median distance of around two kilometers with a high degree of variation in distance. These findings will guide planners, designers, developers, and policy makers in promoting greater levels of walking and cycling and suggests future research directions within this field.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".