A Bi-Level Passenger Preference-Oriented Line Planning Model for High-Speed Railway Operations
Bibliographic record
Abstract
To solve bottlenecks and capacity shortages in railway corridors, it is necessary to consider passengers’ temporal-spatial preferences in conjunction with train dispatching decisions at the line planning stage. A design of the departure and stop plans which is based on the passengers’ preferences would guarantee a high quality of service. This paper presents a line planning optimization model for high-speed railway (HSR) operations that addresses both the operating costs of lines and passengers’ preferences through a bi-level integer programming (IP) problem. Using actual travel data from the Shenzhen–Changsha HSR in China, we investigate passengers’ travel behavior during different time periods and between several origin and destination (OD) pairs to characterize their spatial-temporal preferences. An IP model is then developed that maximizes the degree of passenger departure time satisfaction (DTS) at the higher level and the operating costs of lines at the lower level. Due to the complexity of the problem, a methodology is proposed to find a conflict-free solution for the proposed model to find train frequencies in each block and their stop plans at each station between the upper- and lower-level models. It is shown that a given passenger trip demand between an origin and a destination could become more flexible by analyzing its time characteristics and the DTS. Finally, a case study is presented to show the effectiveness of the model and the solution approach.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".