COVID-19 Outbreak in Colombia: An Analysis of Its Impacts on Transport Systems
Bibliographic record
Abstract
The global COVID-19 outbreak has demanded drastic actions and policies from the governments and local authorities to stem the spread of the virus. Most of the measures involve behavioural changes from citizens to reduce their social contact to a minimum. Thus, these actions influence individual activity patterns and transport systems in different ways. This paper studies the short-term impacts on the transport system caused by the different policies adopted by the Colombian government and local authorities to contain the COVID-19 spread. Using official and secondary data concerning the seven most populated cities in Colombia, we analyse the impacts on three components of the transport system: air transport, freight transport, and urban transport. Results show that national policies and local decisions have decreased the demand for motorised trips across the cities, diminishing congestion levels, reducing transit ridership, and creating a reduction in transport externalities. The country banned air transport for passengers and only allowed air cargo for medical and necessary supplies, which will have negative consequences for the economics of the airline industry. During the first three months of the COVID-19, freight was the most resilient transport component. However, freight trips diminished around 38%, affecting mainly the supply chain of nonessential products. During the pandemic, governments need to provide subsidies to maintain the system supply to avoid crowdedness and promote active transport by allocating less-used street space to cyclists and pedestrians. In the short term, transportation service providers will face a financial crisis, deepened by the pandemic, which will require government assistance for their recovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".