Use of data science techniques for the modelisation of the public bike-sharing system of BIXI in Montreal, Canada
Bibliographic record
Abstract
The final master thesis investigate behavioural patterns of the users of bike-sharing\nsystem BIXI in Montreal. A data mining approach is used by applying clustering\nmethods on open data from BIXI, the Government of Canada and the City of Montreal.\nHierarchical clustering with Ward and Gower is processed to form clusters\nusing only the BIXI variables at first. Then, because the authors believe that contextual\ninformation has a fundamental effect on the user’s behaviour, they investigate\nthe impact of adding such information into the clustering methodology. In the end,\na multi-view clustering method is preferred for its quality of preserving the basic\ncharacteristics of simpler set of clusters (mixing less variables). Afterwards, local\nanalysis are done over some profiles of the classes. A dynamic balancing analysis of\nthe station using geographical representation offer good insight on the movements\nof the bikes associated with a cluster. Then, a complex network approach is also\nused to extract more topological information about the bike-sharing network made\nfrom the stations and the trips. All these transdisciplinary approaches used together\nproduce new operational knowledge that can be useful for the operators and the\nlogistics to overcome the redistribution problem as well as improving the quality of\nthe service.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".