Inclusion persons with disabilities to a public transport system: An integrative decision-aiding approach
Bibliographic record
Abstract
The topic of integrating persons with disabilities in society is widely studied, especially nowadays, as it is estimated that one billion people are living with disabilities. The research focus of this paper is on the inclusion of disabled persons in the public transport system with an emphasis on their full autonomy. Several models in operational research treat this problem, such as transport on-demand with its varieties, the shortest path problem, even though they are often understood to focus on public transport rather than accessibility to the public network itself. Providing a full service to persons with disabilities in a public transport system is a very long process, which involves many participants. The diversity of the needs of disabled persons and the various interactions between these needs raises the level of complexity behind this process. To explore this problem, we used a decision-aiding approach, which allows to better guide the adaptations required from a transportation system while respecting both the issues at stake for stakeholders in the transportation value chain and the needs of people with disabilities. The goal is to put together all existing transportation models for PWD and offer different decision choices depending on the PWD needs and network characteristics. The main goal is to provide complete service to PWD without interruption with the different governing level decisions. The contributions of this article are multifold. First, we use a multidisciplinary approach to develop a matrix of the different fields of decision. Second, the decision-aiding process proposes a comprehensive analysis, which gives the ability to choose at any time the suitable model for the inclusion of disabled persons. Third, we put forth a scheme of the relationship among existing optimization models, depending on the public network information, more precisely on the concrete accessibility on the public network. Finally, yet importantly, the crucial contribution in this paper is the practical implementation of the decision-aiding process.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".