Bibliographic record
Abstract
Since the 1970s, there has been a significant increase in urban rail investment. 139 new urban rail systems, metros and light rail systems, have been built world-wide in the past three decades. These investments were in general planned as instruments to solve transport and land-use problems associated with the extensive use of the car. Very few have been successful in improving transport and the urban environment. Previous research has shown that while most of the new generation urban rail systems have not been very successful, their success could have been enhanced if the co-ordination between transport planning and urban planning had been stronger. However, co¬ordination is very difficult to achieve within the contemporary local government structure and fragmented planning system. In spite of these findings, political support for urban rail systems is still strong, and investment on these systems is very likely to continue. Considering the cost incurred in the development of these systems, to make them successful remains a challenge. \nThis study explores ways of making new urban rail systems more successful. It develops a methodology for analysing the success of systems, identifying the factors behind their success, and enhancing their success. Based on the analysis of new generation urban rail systems, a planning framework is developed. The framework is a policy-based approach to help planners and operators to increase the success of their systems. It has two main functions: it predicts the success of new systems, and makes recommendations on how their success can be enhanced. While the framework addresses many factors that may affect success, there is a special focus on exploring methods for providing and sustaining co-ordination between transport and urban planning. \nThe planning framework is developed through the analysis of eight case studies, four from the United States, one from Canada, and three from Britain. It is then tested on seven other urban rail systems, five from the United States, one from Canada, and one from France. Finally, the framework is applied to recently opened urban rail systems in Britain and Turkey: it predicts how successful these systems are likely to be, and shows how their success can be enhanced.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".