A multi‐objective subway timetable optimization approach with minimum passenger time and energy consumption
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".