The impact of bicycle theft on ridership behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it