Transfer Optimization Model for Intermodal Transit Services into the Suburbs
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
Inter-modal transfer time is a significant component of transit travel from the perspective of passengers, and schedule coordination is one possible strategy to reduce the inconvenience of transfers. This study developed an optimization model for generating transit timetables that minimize transfer-related times. The model attempts to find an optimal timetable by shifting the existing timetable and/or adding holding time to the timetable to optimize the transfers from transit units on a feeder commuter route to transit units on a receiver suburban route. Analytical models are developed to estimate the waiting time of the transfer passengers, and also to determine the influence of the schedule modification on the waiting times of non-transfer passengers. The models explicitly incorporate the variability of transit vehicle arrivals, assumed to follow a lognormal distribution. This study employs Genetic Algorithms (GA) as the solution approach to find an optimal schedule. The developed model is tested using schedule data from a local transit service in the City of Brampton, Canada. The results show that the model reduces effectively the total transfer and waiting times through the modification of the current schedule.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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