Exploring the Role of Shared Mobility in Alleviating Private Car Dependence and On-Road Carbon Emissions in the Context of COVID-19
Bibliographic record
Abstract
Shared mobility is becoming increasingly popular worldwide, and travelers show more complex choice preferences during the post-pandemic era. This study explored the role of shared mobility in the context of coronavirus disease (COVID-19) by comparing the travel mode choice behavior with and without shared mobility. Considering the shared mobility services of ride-hailing, ride-sharing, car-sharing, and bike-sharing, the stated preference survey was designed, and the mixed logit model with panel data was applied. The results show that if shared mobility is absent, approximately 50% of motorized mobility users and 84.62% of bike-sharing adopters will switch to using private car and public transport, respectively. The perceived pandemic severity positively affects the usage of car-sharing and bike-sharing, while it negatively affects the ride-sharing usage. Under different pandemic severity levels, the average probabilities of private car choice with and without shared mobility are 38.70 and 57.77%, respectively; thus, shared mobility would alleviate the dependence on private car in post-pandemic future. It also helps to decrease the on-road carbon emissions when the pandemic severity is lower than 53. These findings suggest policymakers to maintain the shared mobility ridership and simultaneously contain the pandemic. Additionally, pricing discount and safety enhancement are more effective than reducing detour time to protect ride-sharing against COVID-19.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".