Study on Passenger Route Choice Differences between the Urban Rail Transit Station Pairs
Bibliographic record
Abstract
[Objective] For passenger route choices between urban rail transit station pairs in existing models， the passenger flow distribution is often based on uniform assumptions. But in reality， the number of effective routes between two stations and the proportion of passengers choosing each route vary significantly， and influencing factors are complex. Therefore， it is necessary to analyze and identify the influencing factors and law behind differences in passenger route choices. [Method] Based on mobile signaling data， the actual travel routes of China Unicom users as Shanghai urban rail transit passengers are obtained， along with the number of passengers choosing each route. The stability of round-trip routes between rail transit station pairs and the differences in passengers route choice proportions are observed. The causes of the differences in typical round-trip route choice proportions and their impact on passenger travel choices are analyzed from the perspectives of route travel time， interchange conditions， and ride comfort. [Result & Conclusion] Differences in round-trip route choices between urban rail transit station pairs are mainly influenced by factors such as the passengers' familiarity with rail transit network， the route travel time， the relative position of interchange stations， the number of interchanges， the interchange time and walking distance during interchanges， and the available seats in train compartments. The research findings provide a reference for optimizing urban rail transit passenger route choice models.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".