Investigating the effect of handoff algorithms on the performance of CBTC systems using physics‐based propagation models
Bibliographic record
Abstract
The performance of communication‐based train control (CBTC) systems, in terms of frequency of handoffs [changing association from one access point (AP) to another] and duration of service interruptions, strongly depends on the employed handoff algorithm. In this study, the received signal strength used for the calculation of these metrics is generated by a custom vector parabolic equation (VPE) solver explicitly developed to achieve high accuracy in complex tunnel environments. The authors demonstrate that VPE is as effective and accurate as measurements in estimating the performance of CBTC systems. Additionally, they present a methodology for selecting a handoff algorithm that most enhances the performance of a CBTC system, where APs have already been deployed. The impact of handoff algorithms on system performance is investigated by considering multiple system configurations in a 5 km, curved, rectangular tunnel. A multiple‐attribute decision making approach is presented to rank handoff algorithms based on their performance. To validate this proposed simulation‐based framework, a CBTC system deployed in the London Underground is presented, where measurement‐based ranking is compared against the one derived using VPE simulations.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".