Coordination and Optimization of Long-Distance Passenger Departure Timetable Connected to High-Speed Railway Station: Considering the Heterogeneity of Transfer Passengers Demand
Bibliographic record
Abstract
Optimizing the departure timetable of long-distance passenger transport connected to high-speed railway stations can not only improve the attractiveness of long-distance passenger transport, reduce the loss of passengers, but also alleviate the pressure of passenger flow accumulation caused by uneven arrival of high-speed railways. Considering the demand heterogeneity of high-speed railway outbound transfer passengers and analyzing the characteristics of transfer travel time, a multiobjective optimization model with unequal interval departures is established. The model takes the minimum total cost and the highest transport capacity as the goal, with the constraints of the departure interval, the waiting time of the stranded passengers, the amount of passenger loss, etc., to optimize the adjustment of the departure interval and the number of departures for long-distance passenger transport and to answer it with the help of Matlab and Lingo software. The calculation results show that the optimized timetable strengthens the synchronous connection with the arrival of small peaks of passenger flow and improves the matching degree of transportation capacity and passenger flow demand. The total passenger transfer time after optimization is reduced by 10.53 h, and the transfer time per capita is reduced by 189.54 s.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".