Investigating the Impact of the COVID-19 Pandemic on Travel Mode Choice Behaviour – A Stated Preference Case in Wuhan, China
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the impact of the COVID-19 pandemic on travel modes choice behaviour using a case study from Wuhan, China. A SP-experiment based survey was conducted in Wuhan, based on which an MNL model and a latent class MNL model were established, respectively. The model estimation results show the following conclusions. First, the attributes that are normally believed to significantly affect the residents’ travel mode choice behaviour turned out to be insignificant during the COVID-19 pandemic. Second, attributes such as age, gender, driving license, income trend, use frequency of public transit, currently most-frequent-used mode, household size, monthly household income, distance from metro station to home, number of confirmed/deaths cases, vaccination are significantly affecting the respondents’ travel preferences. Third, the outbreak of the COVID-19 pandemic leads to a decline in the residents’ preferences toward public transit, but the promotion of vaccines can lead residents to return to the public transit system. Fourth, the respondents were divided into three latent classes: high-susceptible, medium-susceptible and low-susceptible classes. These conclusions are believed to provide a reference for the investigation of impact of the COVID-19 pandemic or other similar public health events on the transportation system, and also offer supports for policy-making to effectively deal with such pandemics.
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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 | 0.001 | 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 it