The impact of bicycle theft on ridership behavior
Bibliographic record
Abstract
Cities worldwide are promoting bicycling as a sustainable mode of transportation. However, bicycle theft remains a significant deterrent for potential riders, and also influences the behaviors of existing cyclists. Understanding the impact of theft on bicycling behaviors provides a foundation for developing strategies to address the negative impacts of bicycle theft. Our goal is to characterize if and how bicycle theft changes individual bicycling behavior. We gathered responses from 1821 individuals in a survey focused on bicycle theft in North America. We employed bivariate analysis and binary logistic regression models to explore the relationships between demographic factors, bicycle attributes, and pre-theft behavior to explain post-theft bicycling behavior. The results show that 45% of survey respondents reduced or ceased bicycling post-theft, while 6% increased their bicycling. Additionally, 40% transitioned from bicycling to unsustainable modes of transportation for their post-theft trips. Also, 69% of people eventually replaced their stolen bicycles, of which 46% selected models of equal/higher value. Pre-theft bicycling activity emerged as the most influential factor on ridership behavior after a bicycle theft, with occasional riders experiencing the most negative impact, compared to frequent riders, who remained committed to bicycling. Recovery of the stolen bicycles, e-bicycle usage, number of bicycles owned, and income levels were also predictors of future bicycling patterns. The insights from this research can inform targeted interventions for populations most at risk to reduce the negative impact of bicycle theft, such as secure parking for new and low-income bicyclists.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".