How Local Is Main Street? Analysis of Non-Work-Related Trips to Four Commercial Streets in Montréal
Bibliographic record
Abstract
With increasing concern about global climate change and sustainable development, local shopping in pedestrian friendly environments has been promoted as a strategy to reduce travel distances and increase the use of sustainable transportation modes.The resurgence of central neighborhoods and traditional commercial streets has led to livelier streets.Accordingly, an understanding of the current travel pattern to traditional neighborhoods is important for transportation planners, designers and decision-makers, to help them in designing and promoting commercial settings that can reduce trip distances and encourage sustainable transport.This paper focuses on non-work related trips to four traditional main streets in Montréal, Québec, Canada.We use the Montréal Origin and Destination survey to model each individual trip to the studied streets.Three linear regression models are generated to investigate factors influencing trip length for auto users, transit users and pedestrians and cyclists.Different factors are found to have influences on trip distance for users using different modes.For auto users it is found that neighborhood characteristics, trip purpose and type of destinations have an influence on trip distance.This can be contrasted to pedestrians and cyclists, where the destination has a little influence on distance but personal characteristics are found to be the main determining factor.For transit riders, distance is affected by their accessibility to service, activities at destination and personal characteristics.Findings in this paper can help land use and transportation planners and decision-makers in understanding the factors leading to a reduction of trip distances and attracting more nearby customers to main commercial streets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".