An Analytical Alternative to Transit Signal Priority Micro-simulation Modeling: Model and Application
Bibliographic record
Abstract
The estimation of the expected performance of Transit Signal Priority (TSP) for a given intersection is not a trivial task given the large number of factors that influence the TSP operation. Previous studies have demonstrated that TSP is not always guaranteed to provide net benefits and the general traffic particularly on the non prioritized approach, is typically negatively impacted by the deployment of transit signal priority under all types of traffic signal control. In current practice, most traffic impact studies are conducted using microscopic traffic simulation models (such as VISSIM, PARAMICS, AIMSUN, etc.) that provide the ability to model in detail the roadway corridor, traffic signal operations, and vehicle movements (including transit vehicles). However, these simulation models require specialized expertise and significant effort to apply correctly – resources that are often not available in-house to transit agencies. This paper proposes an analytical model which can be used instead of of microscopic simulation models for estimating the impacts (delay, fuel consumption, and emissions) of implementing transit signal priority. The proposed model is suitable for implementation within a spreadsheet and requires considerably less effort, and less technical expertise, to apply than a typical micro-simulation model and yet provides estimates of performance impacts that are consistent with those obtained from simulation modeling. It is typical that the resources available for funding transit priority measures (including TSP) is much less than would be required to deploy TSP at all signalized intersection along even the major transit routes in a transit network. The usefulness of the model is demonstrated through the application of the proposed model to prioritize TSP deployment at sixteen signalized intersections in the Region of Waterloo, Canada. This paper is of particular interest to transit agencies or traffic engineering personnel who are responsible for identifying, evaluating and prioritizing locations for the deployment of transit signal priority.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".