Measuring transit service reliability at the route level? Exploring the relationship between reliability measures and ridership
Bibliographic record
Abstract
Transit agencies are in a consistent struggle to offer an attractive service that draws a higher level of ridership. To improve the attractiveness of the service, one of the key objectives of agencies is to enhance transit service reliability. Service reliability refers to service punctuality and adherence to schedule. A considerable number of studies have focused on understanding the general factors affecting reliability. Nevertheless, it is rare to find studies that explore the association between different reliability measures and transit usage at the route level. Therefore, the aim of this study is to assess how different reliability measures relate to public transit-usage and which measures best explain variations in transit ridership. In total, 22 transit reliability measures that ranged from on-time performance (OTP) measures to service variation measures were assessed. Using land-use, socioeconomic, and detailed ridership datasets, in addition to data obtained from Winnipeg Transit’s Automated Vehicle Location (AVL) system, random coefficients mixed-effect models were estimated at the route level. The results show that, generally, deviation-based measures performed better than OTP measures in explaining transit ridership at the route level. The reliability measure of absolute deviation at terminals performed best in predicting variations in transit ridership, while controlling several influential factors. More importantly, the improvements in predication of ridership due to the use of reliability measures varied according to route’s ridership. This study offers planners and policymakers helpful insights into understanding the relationship between transit service reliability measures of choice and transit ridership at the route level of analysis.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".