Decision Support Framework for Cycling Investment Prioritization
Bibliographic record
Abstract
Considering the significant potential for environmental, economic, social and health benefits from cycling, transport planners around the world are considering a wide variety of strategies for its promotion. However, cycling investments still have to find their place in a coherent package among other policies. Different constraints often imply a need for prioritization in cycling project implementation. The need for prioritization list of proposed investments can be caused by different factors such as available budget, available time, and regulatory constraints. Evaluation of investments in cycling infrastructure is a field of study that still requires further development, as previous research has mostly focused on questions of what to build and where. Previously used cost-benefit methods have substantive and procedural limitations in dealing with non-commensurable effects, and dealing with multiple conflicting objectives stemming from different stakeholders. On the contrary, development of prioritization list is formulated here as a semi-structured decision problem, thus belonging to the group of multi-criteria analysis (MCA) methods. The MCA methodology implemented in this decision-support framework is based on Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). The expert-based decision-support framework includes procedures for defining list of evaluation criteria and their weights, scoring of alternatives, and sensitivity analysis. Presented decision-support framework is applied on six bicycle sections of the EuroVelo route 8 through Montenegro. Results provide a list of prioritized infrastructural investments, as well as list of criteria with weights, and sensitivity analysis. Decision-support framework is discussed in the context of further professionalizing of cycling planning, as well as short-term and long-term structuration of organization learning in the transition country context. Finally, this development opens up directions for further contextualization of decision criteria, and greater consideration of user attitudes in cycling promotion.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".