Multi-objective railway timetabling including energy-efficient train trajectory optimization
Bibliographic record
Abstract
Energy-efficient train driving is an important topic to railway undertakings (RUs) for sustainability and cost reduction. The timetable affects the possibilities for energy-efficient train driving by the amount of running time supplements, which is the topic of energy-efficient train timetabling (EETT). The scientific literature on EETT focuses mainly on the balance between total running time and energy consumption. However, in practice RUs consider a trade-off between the total running time, the infrastructure occupation and the timetable robustness, while energy efficiency is not considered. In this paper we consider a multiple-objective timetabling problem at a microscopic infrastructure level that adds energy consumption to the other three objectives. We approach the multiple-objective problem by a brute force search algorithm, where we use two different methods to compute the optimal solution: a weighted sum method and a distance metric method. We apply the method to a Dutch case study on the corridor between the stations Arnhem Central and Nijmegen with alternating Intercity and Sprinter trains, without intermediate overtaking possibilities. The results indicate that there is a balancing relationship between the total running time and energy consumption, without influencing the infrastructure occupation and robustness. The results of the 10 Pareto-optimal solutions show a variation of 5% for the total running time, 18% for the energy consumption, 0.3% for the extended cycle time, and 0.8% for the buffer time. The shortest running time leads to 18% more energy consumption than the longest running time with 5% more running time supplement. In both cases the extended cycle time and buffer time are almost constant. On the other hand, reducing the infrastructure occupation leads to homogenization of the timetable. Therefore, including energy consumption in the multiple-objective can be used to balance the trade-off between total running time and capacity consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.005 | 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".