Structural Equation Modelling of Household Long-Distance Flexible Travel Behavior
Bibliographic record
Abstract
In recent years, more and more households plan to tourist destinations or visit relatives on holidays.Facing the surging demand for household travel, this paper aims to explore the generation mechanism of household long-distance flexible travel on holidays.Firstly, a questionnaire survey on long-distance flexible travel behavior was conducted among middleincome households in first-and second-tier cities.Then, multiple endogenous and exogenous variables were extracted from the survey data.On this basis, a structural equation model (SEM) was established to analyze the influence of individual attributes, economic attributes, and household attributes over the travel attributes and travel intensity of household long-distance flexible travel.The results show that economic attributes had the greatest impact on travel intensity, among all exogenous variables.This means household income promotes the travel intensity, especially travel duration.Besides, household attributes negatively affect travel intensity.In other words, with the growing number of elderlies, children, and employed in the household, the number of travelers in household travel will increase, while the intensity of household travel will decline; the household will prefer to travel by car.The research results provide theoretical supports to the research of household flexible travel behavior, and enable tourist cities to effectively manage and optimize holiday traffic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".