Cluster Analysis of Daily Cycling Flow Profiles during COVID-19 Lockdown in the UK
Bibliographic record
Abstract
The COVID-19 pandemic and resulting government-enforced lockdown affected the travel behavior and lives of people worldwide. In this research, hierarchical cluster analysis (HCA) is used to quantify the impact on daily flow profiles of cyclists due to the public’s response to different levels of restrictions during a 6-month period of the COVID-19 pandemic in 2020. An inductive loop network in Tyne and Wear, the UK provided cycle flow data from 25 sites. A paired sample t-test was carried out between the “Pre-COVID-19” baseline year and 2020 to determine how cycling volumes changed at each site. The HCA was then performed on the diurnal hourly flow profiles to observe how they changed within the same time period. Finally, the relationship between diurnal flow profile and volume was assessed. Overall cycling volume in the study area increased by 38% during the lockdown. The highest increases were found at coastal sites, with more modest increases in suburban areas and reduced volumes at city center locations. The HCA of the diurnal flow profiles revealed that locations associated with noncommuting-shaped flows witnessed the largest increases while commuting profiles saw a decrease. As lockdown restrictions eased, flow profiles began to revert back to the prepandemic norm but never fully returned to prepandemic levels. The adoption of working from home postpandemic will change commuting behavior. The conclusions drawn from this study suggest consideration of noncommuting trips should be made when planning the design and location of future cycling schemes, and the HCA of flow profiles can assist in this decision-making process as a method to quantify changes in daily flow profiles of cycling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".