Network Structure of Intercity Trips by Chinese Residents under Different Travel Modes: A Case Study of the Spring Festival Travel Rush
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the advent of big data, the use of network data to characterize travel has gradually become a trend. Tencent Migration big data can fully, dynamically, immediately, and visually record the trajectories of population migrations with location‐based service technology. Here, the daily population flow data of 346 cities during the Spring Festival travel rush in China were combined with different travel modes to measure the spatial structure and spatial patterns of an intercity trip network of Chinese residents. These data were then used for a comprehensive depiction of the complex relationships between the population flows of cities. The results showed that there were obvious differences in the characteristics of urban networks from the perspective of different modes of travel. The intercity flow of aviation trips showed a core‐periphery structure with national hub cities as the core distribution. Trips by train showed a core‐periphery structure with cities along the national railway artery as the core. This gradually decreased toward hinterland cities. Moreover, the intercity flow of highway trips indicated a spatial pattern of strong local aggregation that matched the population scale.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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 it