Optimization of the Operation Plan of Airport Express Train with Consideration of Train Departure Time Window
Bibliographic record
Abstract
This paper proposes an optimization model for the train operation scheme of the Airport Express Line (AEL) based on the expected arrival time of passengers by the introduction of the train departure time to cope with the time‐dependent passenger flow and provide better prompt train service according to passengers’ demand. Considering factors such as train sections, station arrangement, passenger capacity, departure time windows, passenger flow conservation, and boarding and disembarkation processes, this paper also aims to find the optimal combination of the passengers’ total travel time and the train operation cost. A set of alternative train options is introduced to simplify the model and convert integer variables related to train pairs into 0‐1 variables. The elaborately designed simulated annealing algorithm mainly focuses on the key elements of strategies like initial solution generation, neighborhood solution construction, and the allocation of passenger flows, tailored to the model’s unique features and the time‐dependent passenger flow. Neighborhood solution strategies include the increase or haut of train operations and the adjustment of the number of stops, which refines the solution space and boosts the process efficiency of the heuristic algorithm. Additionally, the model and algorithm proposed in this paper are practiced during the peak hour of Nanjing Metro Line S1 for empirical validation. The research findings demonstrate that the optimized train operation scheme is better synchronized with the fluctuating number of time‐dependent passenger flows and exhibits notable improvement in computational efficiency and convergence.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".