The Multimodal Accessibility Target (MAT)
Bibliographic record
Abstract
If an infrastructure intervention, such as the conversion of a car lane into a dedicated bus lane, decreases automobile accessibility but increases transit accessibility, what is the overall effect on accessibility? To date, no methods have been proposed to evaluate this question. Accessibility research in the transportation and land-use literature has been dominated by unimodal and comparative approaches to analyzing accessibility. Little attention has been paid to quantifying multimodal accessibility or to the interactions between modes, and how these might affect overall accessibility. Moreover, the use of targets for accessibility in planning has been largely ignored. Particularly important to the evaluation of transportation planning outcomes is accessibility to employment, as this is a key element and predictor of urban economic prosperity. This study, building on the work of Alain Bertaud, proposed the multimodal accessibility target or MAT. The MAT provides a more accurate picture of overall (across mode) accessibility to jobs in a city, and can be used to evaluate transportation infrastructure investments. It also provides a target for accessibility that is an easily interpretable indicator that can serve as a goal in accessibility-based transportation planning. A case study of the proposed implementation of a bus rapid transit (BRT) system in Montreal, Canada was used as an empirical example of the use of the MAT. In the case study, predicted multimodal accessibility to employment in the study area (and thereby the MAT) was found to increase with BRT compared with the base case scenario.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".