Mining Travel Time of Airport Ferry Network Based on Historical Trajectory Data
Bibliographic record
Abstract
An airport ferry vehicle is a ground service vehicle used to transfer passengers between the far apron and the terminal. The travel time of ferry tasks in the airport ferry network is an important decision-making basis for ferry vehicle scheduling. This paper presents a graph-based method to mine the travel time between nodes in the airport ferry network. Firstly, combined with map and trajectory information, the method takes the terminal boarding gates, parking lots, and remote stands as road network nodes to build a complete airport ferry road network. Then, this paper uses big data processing technology to identify the travel time between regional connection nodes by data fusion through the temporal and spatial relationship between flight schedule and ferry vehicle GPS travel trajectory. Finally, the Floyd shortest path algorithm in graph theory is used to obtain the shortest path and travel time of all OD points. The experimental results show that all the ferry times calculated by the method proposed in this paper can better reflect the actual driving situation. This method saves the manpower, material resources, and time cost of on-site investigation and lays a foundation for the scheduling of ferry vehicles.
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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".