Integrated smart feeder/shuttle transit service: simulation of new routing strategies
Bibliographic record
Abstract
SUMMARY The idea of designing an integrated smart feeder/shuttle service stemmed from the need to overcome the problem of using an excessive number of cars arriving and parking at a train station within the same time span. This problem results in high parking demand around the train station. Moreover, some potential train riders will, instead, use their cars and hence become a party to increasing the traffic congestion. This work develops a new idea of an integrated and innovative feeder/shuttle system with new operating and routing concepts. The fulfilled objectives are as follows: (i) to construct and examine different operating strategies from both the user and operator perspectives; (ii) to examine different routing models and scenarios; and (iii) to construct a simulation tool for (i) and (ii). Ten different routing strategies are examined, with all the combinations of fixed/flexible routes, fixed/flexible schedules, a unidirectional or bidirectional concept, and shortcut (shortest path) and/or short‐turn (turnaround) concepts. These strategies are investigated by employing a simulation model specifically developed and constructed for this purpose. This simulation model is used in a case study of Castro Valley in California in which the feeder/shuttle service is coordinated with the Bay Area Rapid Transit service, and the 10 routing strategies are compared in regard to four fleet‐size scenarios. One of the interesting results found is that the fixed‐route and flexible‐route concepts are comparable in performance measures when applying a combination of operating strategies. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.001 |
| 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 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".