A multi‐objective subway timetable optimization approach with minimum passenger time and energy consumption
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Bibliographic record
Abstract
Summary Considering the service quality and energy efficiency, this paper develops a multi‐objective timetable optimization approach for subway system. First, we analyze the variation on the passenger flow at stations, and propose the concept of passenger waiting time. Second, we develop a speed‐profile‐generation approach to search for the energy‐efficient speed profile under the condition of a given section trip time. Then we formulate a multi‐objective timetable optimization model to minimize the passenger time and energy consumption by controlling section trip time and station dwell time, in which passenger time includes both waiting time and traveling time. We respectively employ the ideal‐point compromise approach, linearly weighted compromise approach and fuzzy linear programming approach to find the suboptimal solution, via performing a genetic algorithm. With the operation data from Beijing Yizhuang and 4‐Daxing subway lines of China, we conduct extensive case studies to demonstrate the effectiveness of our model. The results show that the passenger waiting time and energy consumption can be reduced during both peak and off‐peak hours. The proposed model and algorithm can be developed to a decision support system for dispatchers to schedule trains in the real world. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it