Mitigating Increased Driving after the COVID-19 Pandemic: An Analysis on Mode Share, Travel Demand, and Public Transport Capacity
Bibliographic record
Abstract
Reduced transit capacity to accommodate social distancing during the COVID-19 pandemic was a sudden constraint that along with a large reduction in total travel volume and a shift in activity patterns contributed to abrupt changes in transportation mode shares across cities worldwide. There are major concerns that as the total travel demand rises back toward prepandemic levels, the overall transport system capacity with transit constraints will be insufficient for the increasing demand. This paper uses city-level scenario analysis to examine the potential increase in post-COVID-19 car use and the feasibility of shifting to active transportation, based on prepandemic mode shares and varying levels of reduction in transit capacity. An application of the analysis to a sample of cities in Europe and North America is presented. Mitigating an increase in driving requires a substantial increase in active transportation mode share, particularly in cities with high pre-COVID-19 transit ridership; however, such a shift may be possible based on the high percentage of short-distance motorized trips. The results highlight the importance of making active transportation attractive and reinforce the value of multimodal transportation systems as a strategy for urban resilience. This paper provides a strategic planning tool for policy makers facing challenging transportation system decisions in the aftermath of the COVID-19 pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".