Prioritization of Cycling Infrastructure Expansion: A Data-Driven, Multi-Criteria Approach
Bibliographic record
Abstract
Safe, well-connected infrastructure is essential to increasing the share of cycling as an urban mode of transport. Yet cities often face budgetary and scheduling constraints when deciding where to expand their cycling networks. This paper presents a data-driven framework for prioritizing cycling infrastructure investments using GPS records from Tartu’s public bicycle-sharing system (PBSS) collected between 2020 and 2023. The dataset comprises nearly three million trips and approximately 330 million location points with trip identifiers. After map matching and spatial aggregation using H3 hexagonal grids, we analyze cycling activity at fine spatial resolution. We develop three complementary prioritization strategies, current demand, potential demand, and network connectivity, to identify critical network gaps and compare them with the city’s existing master plan. Finally, we apply the MULTIMOORA multi-criteria decision-making method to integrate these strategies and produce a ranked list of candidate bikeway segments. The combined analysis highlights several high-priority corridors not captured in current planning documents. Our results illustrate how large-scale micromobility data, combined with multi-criteria decision analysis, can inform efficient, user-oriented, and evidence-based cycling infrastructure planning in smart urban environments.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".