Bibliographic record
Abstract
Since the 1970s, there has been a significant increase in urban rail investment. 139 new \nurban rail systems, metros and light rail systems, have been built world-wide in the past \nthree decades. These investments were in general planned as instruments to solve \ntransport and land-use problems associated with the extensive use of the car. Very few \nhave been successful in improving transport and the urban environment. Previous \nresearch has shown that while most of the new generation urban rail systems have not \nbeen very successful, their success could have been enhanced if the co-ordination \nbetween transport planning and urban planning had been stronger. However, coordination \nis very difficult to achieve within the contemporary local government structure \nand fragmented planning system. In spite of these findings, political support for urban rail \nsystems is still strong, and investment on these systems is very likely to continue. \nConsidering the cost incurred in the development of these systems, to make them \nsuccessful remains a challenge. \nThis study explores ways of making new urban rail systems more successful. It develops \na methodology for analysing the success of systems, identifying the factors behind their \nsuccess, and enhancing their success. Based on the analysis of new generation urban rail \nsystems, a planning framework is developed. The framework is a policy-based approach \nto help planners and operators to increase the success of their systems. It has two main \nfunctions: it predicts the success of new systems, and makes recommendations on how \ntheir success can be enhanced. While the framework addresses many factors that may \naffect success, there is a special focus on exploring methods for providing and sustaining \nco-ordination between transport and urban planning. \nThe planning framework is developed through the analysis of eight case studies, four \nfrom the United States, one from Canada, and three from Britain. It is then tested on \nseven other urban rail systems, five from the United States, one from Canada, and one \nfrom France. Finally, the framework is applied to recently opened urban rail systems in \nBritain and Turkey: it predicts how successful these systems are likely to be, and shows \nhow their success can be enhanced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".