Performing Structural Equation Modeling in Public Transport Through COVID-19 Pandemic Time
Bibliographic record
Abstract
Traveling via public transportation is a better option than driving a private car and should be encouraged to reduce congestion, pollution, and fuel costs.However, due to COVID-19 pandemic, governments restrict the use of public transportation in order to limit infection spread.This study intends to identify travelers' attitudes and preferences for using public transportation during the COVID-19 epidemic using structural equation modeling (SEM).A questionnaire survey was created to analyze travelers' behavior, attitudes, perceived risk, and sense of responsibility when utilizing public transportation in Baghdad, Iraq.234 complete responses were analyzed using the Structural Equation Modeling technique.The survey findings and measurement equations supported the relationship between observable and latent variables.The SEM results demonstrated that travelers' Perceived (PER) and Responsibility (RES) are favorably connected to Attitudes (ATT), whereas Behavior (BEH) towards public transportation is adversely related to Attitudes (ATT).To assess the confirmatory of measurement scale, confirmatory factor analysis (CFA) measurement was combined with nine Goodness-of-Fit measurements: Chi-square, Chi-square/df, RMR, GFI, AGFI, NFI, TLI, CFI, and RMSEA.This study highlights the findings of structural equation modeling research of using public transport through pandemic time in term of travel behaviors to improve the quality of service of public transport.
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 | 0.009 | 0.017 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".