Evaluation of Subway Bottleneck Mitigation Strategies using Microscopic, Agent-Based Simulation
Bibliographic record
Abstract
Many subway systems operating near capacity face challenges in meeting reliability and level of service targets. This paper examines the effectiveness of various strategies to relieve congestion and increase capacity using a microscopic, agent-based, urban heavy rail simulation model. The Massachusetts Bay Transportation Authority’s (MBTA) Red Line serves as the testbed for the analysis. The Red Line operates very close to its capacity. Bottlenecks on the Red Line and possible strategies to mitigate them are discussed, including skip-stop, station consolidation, and dwell time control. The results show that, compared with the no strategy case, skip-stop and consolidation are effective in reducing runtimes and passenger journey times, increasing train throughput, and maintaining headway regularity during peak periods. Performance under these two strategies is also robust to dispatching irregularity and increases in passenger demand. The dwell time control strategy mitigates congestion and disturbances in operations to some extent, but is less effective and robust.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".