When Blockchain Meets Urban Rail Transit: Current Prospects, Case Studies, and Future Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thanks to the vigorous development of artificial intelligence, urban rail transit (URT) is undergoing a new round of intelligent upgrades. While its intelligence level is improving, URT suffers from a weak trust foundation, high data sharing costs, and low collaboration efficiency. Driven by outstanding features of decentralization, resilience against tampering, and traceability, blockchain can provide a safe and efficient value-trust exchange infrastructure for URT. This article focuses on the current prospects, case studies, and future challenges of blockchain-empowered URT. We first introduce blockchain fundamentals and mainstream blockchain platforms, comparing the technology’s advantages and highlighting the motivation of using it in URT. The prospects of using blockchain in the lifecycle of URT, which includes planning and construction, operation and management, control and security, and upgrading and transformation, are explored. Furthermore, a concrete case study of using blockchain in a distributed authentication scheme for URT is described. Extensive testing results show that the proposed blockchain-based distributed authentication scheme can enhance the security of the train control system without sacrificing communication performance. Finally, we summarize the challenges and problems when using blockchain in future URT systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it