Visualizing public transit system operation with GTFS data: A case study of Calgary, Canada
Bibliographic record
Abstract
Public transportation agencies are one of the industries that generate a large volume of data on a high frequency and velocity basis. The General Transit Feed Specification (GTFS) is one of the datasets these agencies generate and share openly with the public. GTFS feeds contain data for scheduled transit service including stop and route locations, and schedules information. This paper aims to demonstrate the potential of GTFS data, specifically, the paper describes the development of a GTFS data visualization tool that displays spatial and temporal patterns of transit services from which qualitative information and insights can be gained. In this paper, GTFS data from Calgary Transit was used as a case study. Previous studies focused on the development of visualization tools that display transit movement, or static graphical representation of transit operation. However, there is still a need for a dynamic interactive visualization tool that can also measures and displays transit system operation geographically and statistically. This study builds on the previous investigations and further develops a new public transit system operation visualization tool (called PubtraVis) with six visualization modules that reflect on different transit system operational characteristics; mobility, speed, flow, density, headway, and analysis. The user can evaluate two modules side by side for comparative analysis. The analysis module provides an insightful statistical summary and similarity measure and clustering results based on the transit operation characteristics. PubtraVis was tested with real-world users through a user experience study from which it was found to be useful and easy to start using. PubtraVis can be a useful tool to demonstrate the dynamism of transit vehicles from the entire transit network at a glance, and can be used to facilitate communication between transit operators, city authorities, and the general public regarding the public transit planning and operation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".