Optimizing Amenities Allocation at Bus Transit Stops: A Focus on Passengers’ Safety and Comfort
Bibliographic record
Abstract
Ensuring passenger safety and comfort in public transportation is crucial for sustaining ridership and enhancing urban mobility. Crime concerns at transit stops can deter usage, while inadequate stop amenities contribute to discomfort and longer perceived wait times. This study addresses these challenges by developing an optimization framework to strategically allocate resources for Closed-Circuit Television (CCTV) placement and stop amenity improvements. By integrating ridership patterns, crime frequency, and cost considerations, the research formulates an integer linear programming model to enhance transit security and service quality. Additionally, clustering techniques classify transit stops based on crime profiles to ensure targeted safety enhancements. The methodology is applied to Calgary Transit, focusing on Route 32 as a case study. Multiple scenarios are explored to assess the balance between cost, crime mitigation, and passenger demand. Findings indicate that crime frequency significantly influences CCTV prioritization for safety, while improved stop amenities can enhance comfort and convenience for increasing ridership. Despite limitations such as lack of data and the analysis of a lower-demand route, the results provide actionable insights for transit agencies. This study contributes to the field by offering a decision-support tool that helps optimize resource allocation, balancing safety, comfort, and financial constraints. Future work could refine the model by incorporating detailed stop-level crime data and detailed analytics of land use to enhance planning for transit security and amenity investments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".