Optimizing Smart Bike-Sharing Station Placement Near Public Transit Stops Using GIS: A Case Study of London, Ontario
Bibliographic record
Abstract
This research aims to develop a more effective framework for integrating a smart bike-sharing system with the existing public transit network to improve overall connectivity and operational efficiency. We introduce a framework for identifying the best locations for bike-sharing stations near specific bus stops. We deploy four different methods, prioritizing stops with high traffic and easy access and those on the city's outskirts with long wait times and limited access. Our primary focus was on London, Ontario, due to its significant potential for incorporating an alternative mode of transportation alongside its current bus system. This potential is highlighted by the city's large student population, the lack of bike rack facilities, and the absence of a smart bike-sharing system. By setting a 500-meter spacing buffer between bike stations, the study revealed that using one or two methods for station placement yielded the least effective results regarding station counts and potential user reach. However, combining multiple criteria to account for varied user behaviour and placement priorities markedly improved outcomes, suggesting an ideal network with 306 stations serving approximately 349,478 individuals, or 92.70% of the total accessible population.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".