The Changes in the Travel Characteristics Due to Corona Virus Pandemic: A Case Study in Al-Qadisiyah Governorate, Iraq
Bibliographic record
Abstract
Many measures have been imposed by governments around the world to limit the spread of COVID-19.Iraqi authorities adopted a continuous cycles of reinforcement and relaxation in the measures, thus travel behaviors are significantly affected as a result of these measures.This study focuses on the impact of the COVID-19 pandemic on travel pattern in Al-Qadisiyah governorate, Iraq, during three different phases, one phase before pandemic and two phases during the pandemic to study the impact of the variation in imposed measures.Data were collected through a qualitative interviews and online questionnaire surveys that included questions on primary purpose of trip, mode choice of transport, frequency of trips before and during pandemic (including two phases).Descriptive analysis and inferential statistical analysis (especially nonparametric tests) were conducted to analyze the collected data.Results show a significant shifting from work/studying trips to shopping and others trips purposes during the first phase, where distance education and teleworking were imposed.In the second phase, the recommendation for work and study from home were lifted thus the primary trips for work or study purposes have resumed somewhat like their previous pace but with less frequent than its usual in pre-pandemic.A significant reduction in public transport and car-sharing usage was observed during first phase-COVID-19 compared to pre-COVID-19 and second phase-COVID-19.People gave the pandemicrelated factors a higher priority than general factors when selecting a transport mode during the pandemic.However, they put less priority for such factors during second phase of COVID-19 as compared to first phase, due to relaxation in the imposed measures.Changes in travel behavior during a pandemic have an impact on travel demand and favorable transport modes, thus understanding this behavior can help in transportation planning, and decision-making.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".