Impact of COVID-19 Pandemic on Motorcycle Purchase in Dhaka, Bangladesh
Bibliographic record
Abstract
The impacts of COVID-19 on the transportation system have received attention from researchers all over the world. Initial findings reveal that patronage of public transport has gone down, while the use of active transport has increased in general. To the best of our knowledge, no study has focused on the pandemic’s effects on motorcycle mode, let alone in the context of an Asian city. We attempted to fill this void in literature by investigating if COVID-19 has influenced people to purchase motorcycles and determining the factors driving their intentions. The study is based on an online survey of 368 people in Dhaka, the capital of Bangladesh. The study found that around 46% of the respondents were expected to increase travel by motorcycle during the post-lockdown period. About 21% of the respondents were also expected to do the opposite. Around 31% of the respondents planned to purchase a motorcycle by August 2021, and the results indicated that the pandemic has influenced more people to purchase motorcycles compared to the pre-pandemic period. The study further identified factors that influenced the respondents’ plan for purchasing a motorcycle during the post-lockdown period applying the binary logistic regression. Based on the findings of the study, policy measures were proposed for controlling the growth of motorcycle numbers and increasing the use of active transport modes as its alternative, and consequently, helping to achieve sustainable transportation outcomes.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".