Bus transit service reliability: Understanding the impacts of overlapping bus service on headway delays and determinants of bus bunching
Bibliographic record
Abstract
To retain and attract new riders, transit agencies are frequently in search for ways to improve system reliability. Transit agencies typically operate several routes that partially or fully overlay (or overlap) one another in order to offer a better service coverage to reach various destinations. While previous research has focused on understanding the general factors that impact headway adherence and delay, there has been little effort to address the effects of overlapping bus routes on service headway adherence and service bunching. This research investigates the impacts of bus route overlapping on service headway delay and probability of bunching at the stop-level of analysis. The study uses automatic vehicle location (AVL) and automatic passenger count (APC) systems data collected from TriMet, the public transit provider for Portland, Oregon, USA, along one of its heavily utilized bus corridors, the Barbur bus corridor. It is shown that service overlapping can increase headway delay by 3.8 seconds, with no impacts on service bunching. It is also shown that headway delay is a function of scheduled headway between trips. Thus, scheduling more time between trips decreases the service delay, with a minimum of delay occurring at 20 minutes. Trips starting late at the beginning of a route increase the odds of bunching for the following trip on schedule more than its delay. This study offers transit agencies and schedulers a better understanding of the effects of service overlapping on service headway delays from schedule and the determinants of us bunching, which are important components of transit service reliability.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".