Motorisation et localisation : quels effets sur le choix du modal ?
Bibliographic record
Abstract
It is widespread acknowledged that car access and urban sprawl are related to each other and that they both influence modal choice. Nevertheless, few methods have the ability to correctly account for these correlations. In this perspective, the method proposed by BONNEL (2000) is quite unique and relevant since it allows separating the effects of each variable. Up until now, the method has been applied and validated using Lyon and related household survey data as a case study. The purpose of this study is to transpose the approach to a North-American city. This experimentation, on the one hand, allows identifying and measuring the respective influences of locations and car access on the evolution of transit in Montreal. On the other hand, it allows comparing the results from the two cities and discussing the ability of the method to clarify this issue in various urban contexts. The microdata from the large-scale origin-destination household surveys held in the Greater Montreal Area are used for this purpose. This experience confirms that the mathematical approach proposed by BONNEL is capable of distinguishing between the respective impacts of the examined variables. Hence, it also confirms that urban sprawl (with respect to trip ends) has a more important impact than the increase of car ownership on the share of transit. Now that its consistency has been demonstrated with two independent sets of data, the method can be used and developed in various ways, namely by introducing new variables (transportation supply), splitting the population into significant segments or using a more precise zoning system.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.113 | 0.008 |
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".