Small Steps, Big Differences: Assessing the Validity of using Home and Work Locations to Estimate Walking Distances to Transit
Bibliographic record
Abstract
Walking to and from public transport can form a seamless way to integrate physical activity into our daily lives, thereby helping us achieve the recommended minutes of physical activity. To measure the link between physical activity and public transport use, it is critical to determine how far individuals are walking, and are willing to walk, to different modes of transit. Few planners, however, have access to detailed information on the exact public transport lines used by individuals, and therefore need to estimate distances by making use of only home and work locations. This study therefore compared two methods of calculating walking distances: one method using widely available home and work locations and a fastest route algorithm leveraging general transit feed specification data, and a second employing a detailed travel survey containing information on the real routes used by each respondent from Montreal, Canada, to generate more robust estimates of the distances individuals are walking to public transport stops. Results show that walking distances calculated from commonly available origin and destination information tend to underestimate real walking distances by 10%. Multilevel mixed-effect regression models indicate these differing results are mainly attributable to differences in travel behaviour and mode choice. Findings from this study provide a better understanding of how modelled and real walking routes to public transport stops differ, which could be of interest to professionals and urban decision makers wishing to correctly model walking to transit in their region when only limited information is available.
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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 | 0.032 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".