The Hierarchy of Cycling Needs: Modeling the self-assessed propensity to bicycle
Bibliographic record
Abstract
As car dependent cities desire a transition to more sustainable and healthful transportation systems, they need guidance on how to support the adoption of cycling. In this research we measure and model the key factors that lead to a progressive behavioral change towards cycling, using Lisbon as the case study. Based on stated responses from a survey (n = 1079), sub-groups of potential cyclists were identified based on sociodemographic information, cycling experience, and their self-assessed willingness to adopt bicycling. Three binary logit models were calibrated to model the probability to shift between behavior-change stages: from “Pessimist” to “Optimist”, then to “Enthusiast”, and, finally, to “Cyclist”. Results suggest that cycling infrastructure and equipment have a greater effect during the earlier stages of change, while facilities and practical needs have more impact during the middle stages. Finally, the individual’s social network and personal concerns and attitudes are crucial for the final push towards changing behavior and taking-up cycling. Based upon these results, a Pyramid of Cycling Needs is proposed, summarizing the hierarchy of needs to cycling. This framework informs which interventions and policies can have the greatest impact at each different stage of the transition to bicycling, and thus, raise cycling levels if those needs are made redundant. This research is a contribution towards understanding of how a city may transition to a higher cycling maturity level, by adopting an approach of targeted policies to different population groups who are willing to bicycle but have different needs.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".