Analyzing Public Transit Schedule Deviations: A Case Study on Montreal Using Real-Time Data
Bibliographic record
Abstract
Metropolitan cities heavily rely on Intelligent Transportation Systems (ITS) to enhance the overall well-being of their citizens. Despite the implementation of various policies and strategies aimed at improving the reliability and quality of public transportation services, transit authorities consistently face criticism from commuters. The main cause of dissatisfaction arises from deviations in scheduled bus arrival times, leading to either early or late arrivals that disrupt the schedules of commuters. These deviations can result in missed appointments, prolonged wait times at bus stops, and instances of being late for work. This paper provides a preliminary analysis of the public transit system in Montreal City, focusing on delays and deviations. It utilizes planned and real-time transit data to quantify, locate, and classify deviations as systematic (i.e., deviations that are accommodated in the schedules by the transit authority) or stochastic (unforeseen deviations, e.g., due to sudden road accidents). The paper also explores using machine learning models to predict stochastic delays.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".