Urban cycling-specific active transportation behaviour is sensitive to the fresh start effect: triangulating observational evidence from real world data
Bibliographic record
Abstract
BACKGROUND: This study determined if cycling-specific active transportation (AT) was sensitive to the behavioural economics heuristic “The Fresh Start Effect”, with the beginning of a work week being temporal landmark for cycling to work. METHODS: We triangulated data from five sources to test the study hypothesis. First, publicly available cycling traffic data collected from May to September between 2014 and 2019 using electromagnetic counters (EcoCounter Inc, Montreal Qc.) were used to categorize 5 urban trails as “AT” or “leisure” based on hourly cycling traffic patterns. Linear regression model with repeated measures, compared daily trends in cycling traffic over the course of a work week along the different trail types and then compared with daily trends in occupational bicycle parking (n = 56,307 counts), vehicular traffic (n = 6.2 M counts), and sales from a local coffee shop (n = 166,753 counts) over the same time frame. Effect sizes were compared to daily trends in fitness centre attendance (n = 563,290 counts), a positive control for the Fresh Start Effect. RESULTS: We found a significant ~ 22% decline in daily cycling traffic on both AT (-147 cyclists/day; 95% CI: -199.0 to -95 cyclists/day) and leisure trails (-22 cyclists/day; 95% CI: -59 to + 15 cyclists/day) over the course of a work week. The relative decline over the work week in AT-based cycling traffic was similar to the decline in daily parking (~ 14%; -12 cyclists/day; 95% CI: -17 to -7 cyclists/day). The relative effect size of this trend was nearly identical to the decline in fitness centre attendance over the work week (~ 21%; -592 visits/day; 95% CI: -759 visits/day to -425 visits/day), replicating the original Fresh Start Effect. In contrast to the decline in AT-based cycling traffic, daily vehicular traffic (+ 2248 cars/day; 95% CI: 2022 to + 3674 cars/day) and coffee sales (+ 31 units/day; 95% CI: +22 to + 42 units/day) increased ~ 7% from the beginning to the end of a work week. CONCLUSIONS: The weekly patterns of AT-based cycling are sensitive to the Fresh Start Effect. This observation could be used to inform policies for increasing cycling rates in urban centres.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".