Effective, Comfortable, and Sustainable Railway Systems: \nDecision Models for Optimal Asset Management and Scenarios \nAnalysis
Bibliographic record
Abstract
Millions of passengers worldwide rely on a fast, competitive, and reliable transit system for daily transportation. The American infrastructure report card 2017 assigned a level “D-” to the USA’s transit sector that means “poor” condition. The Canadian report card in 2016 assigned a grade of “Fair” to fixed assets (e.g. stations and tunnels) of the transport system; this indicates that such assets “require attention”. Meanwhile, 25% of such fixed assets were ranked in poor and very poor condition. In periods of 2016 – 2018, the Société de Transport de Montréal (STM) has invested the amount of C$2.2 billion, or 78% of its total capital expenditure for metro system maintenance and upgrading. Extensive deterioration of already aged metro systems in North America complicates managing the network while coping with the increased demand and the corresponding need to plan for capital upgrades with a restricted annual budget. Effective planning to rehabilitate existing assets and expand new ones while respecting constraints is key to the success of transit-oriented strategies. However, without a comprehensive multi-criteria decision-making procedure, it is impossible to achieve the optimal actions at the right time within the given budget. The main objective of this research is to develop a comprehensive model for managing urban railways, such as the metro, that supports strategic decisions to maintain the highest level of convenience, safety, comfort and reliability in the metropolitan area. To overcome the gaps found in the literature, these proposed steps should be used: \nStep I: Developing an understanding of convenience with special concentration on the level of service from the passenger’s perspective. The idea is to model, quantitatively and practically, aspects relevant to the user convenience for transit vehicle’s comfort. Step II: Development of a decision-making model to mimic the operation of the transit systems capturing indirect impacts such as human development and sustainability. Step III: Development of an optimization model to analyze investment scenarios for the upgrade and expansion of the railway network, while up-keeping the existing operation at acceptable levels of service, guiding policies, and respecting budget limitation. This includes the relationships between the transit system and human development issues, addressing fighting poverty; supporting accessibility to health, education and job centers; and encouraging the modal shift away from the automobiles. \nThe proposed models could also be used by public transit systems such as Tramway, Bus Rapid Transit (BRT), Light Rail Transit (LRT), traditional buses and metro to guide planning for their maintenance, upgrade and expansion to achieve higher levels of convenience and reliability encouraging transit ridership.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".