An equity‐based passenger flow control model with application to Hong Kong‐Shenzhen border‐crossing
Bibliographic record
Abstract
Abstract Cross‐border passengers from Hong Kong to Shenzhen by the east Kowloon‐Canton Railway (KCR) through the Lo Wu customs exceed nearly 200 thousand on a special day such as a day during the Chinese Spring Festival. Such heavy passenger demand often exceeds the processing and holding capacity of the Lo Wu customs for many hours a day. Thus, passengers must be metered off at all entrance stations along the KCR line through ticket rationing to restrain the number of passengers waiting at Lo Wu within its safe holding capacity. This paper proposes an optimal control strategy and model to deal with this passenger crowding and control problem. Because the maximum passenger checkout rate at Lo Wu is fixed, total passenger waiting time is not affected by the control strategy for given time‐dependent arriving rates at each station. An equity‐based control strategy is thus proposed to equalize the waiting times of passengers arriving at all stations at the same time. This equity is achieved through optimal allocation of the total quota of tickets to all entrance stations for each train service. The total ticket quota for each train service is determined such that the capacity constraint of the passenger queue at Lo Wu is satisfied. The control problem is formulated as a successive linear programming problem and demonstrated for the KCR system with partially simulated data.
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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 | 0.002 |
| 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".