Development and Evaluation of Adaptive Transit Signal Priority Control with Updated Transit Delay Model
Bibliographic record
Abstract
Transit signal priority (TSP) strategies are widely used to reduce bus travel delay and to increase bus service reliability. State-of-the-art strategies enable dynamic (and optimal), rather than predetermined, TSP plans to reflect real-time traffic conditions. These dynamic plans are called adaptive TSP. Existing adaptive TSP strategies normally use a performance index (PI), which is a weighted summation of all types of delays, to evaluate each candidate TSP plan and the weights to reflect the corresponding priority. The performance of an adaptive TSP depends on three factors: delay estimation, weights determination, and optimization formulation. In this context, there are three key academic contributions: (a) an enhanced bus delay estimation model based on advance detection, (b) a mechanism to adjust the PI weights dynamically to reflect the changing necessity of TSP under different conditions, and (c) TSP optimization formulated into a quadratic programming problem with an enhanced delay-based PI to obtain global optimization with the use of MATLAB solvers. In addition, an adaptive TSP simulation platform using a full-scale signal simulator, ASC/3, in VISSIM was developed. The optimal TSP plans were granted or were rejected on the basis of TSP events, such as check-in, check-out, and multiple TSP requests. Through a case study in VISSIM, this research found that, compared with conventional active TSP strategies, the new adaptive TSP strategy could further reduce bus travel time while maintaining a better balance of service on non-TSP approaches along a 7.4-km bus corridor in Edmonton, Alberta, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".