Air Transport versus High-Speed Rail: An Overview and Research Agenda
Bibliographic record
Abstract
The development of high-speed rail (HSR) services throughout the last decades has gradually blurred the concept of competition and cooperation with air transportation. There is a wide range of studies on this subject, with a particular focus on single lines or smaller regions. This article synthesizes and discusses recently published studies in this area, while aiming to identify commonalities and deviations among different regions throughout the world, covering services from Europe, Asia, and North America. Our meta-analysis reveals that the literature is highly controversial and the results vary substantially from one region to another, and a generalization is difficult, given route-specific characteristics, such as demand distribution, network structure, and evolution of transportation modes. As a major contribution, we propose a list of five challenges as a future research agenda on HSR/air transport competition and cooperation. Among others, we see a need for the construction of an open-source dataset for large-scale multimodal transport systems, the comprehensive assessment of new emerging transport modes, and also taking into account the resilience of multimodal transport systems under disruption.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".