Managing Unplanned Rail Disruptions: Policy Implications and Guidelines Towards an Effective Bus Bridging Strategy
Bibliographic record
Abstract
Bus bridging is a key strategy used by transit agencies to handle rail service interruptions. In practice, buses are dispatched from scheduled services to act as temporary shuttles along the disrupted rail segment. This study provides a robust analysis of four factors affecting bus bridging policies: 1) initial dispatch direction of shuttle buses, 2) dispatch time (i.e., the response time for requesting shuttle buses), 3) uncertainty in predicting the incident duration, and 4) reduction of metro passengers demand because of disruption. A user delay modeling tool is used to assess various bus bridging policies based on their resulting users’ delays (for affected passengers) and other system performance measures. The tool was validated, and sensitivity analysis was conducted based on real disruption scenarios that suspended various segments of the metro service in the City of Toronto. The main results indicate that: 1) the initial dispatch direction of shuttle buses should take into consideration the demand at the disrupted segment while maintaining a moderate level of shuttle bus utilization; 2) a 1-min increase in the dispatch time causes about 0.4 min additional waiting time at disrupted metro stations per passenger; 3) incidents with high forecasting errors can cause excessive delays for metro passengers and significant wasted time of non-utilized shuttle buses; and, 4) significant users’ delay savings are observed at higher demand reduction levels. This paper provides transportation practitioners and planners with a better understanding of the different aspects of bus bridging policies based on users’ delays and shuttle buses’ performance metrics.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".