Development of an optimized strategy for integrated traffic and transit signal control
Bibliographic record
Abstract
This dissertation presents an innovative optimized strategy for Integrated TRAffic and TRAnsit signal Control (ITRAC). The development of ITRAC progressed through three phases each offering a stand-alone contribution while offering a foundation for the following phase. Phase one focused on the development of a genetic optimization procedure for traffic signal control without transit signal priority. Phase two focused on the development of an advanced rule-based transit signal priority system that does not optimize timing plans for traffic. It assumes a traffic signal control system running in the background, and provides transit signal priority in a way that reduces negative impacts on traffic. This system is named TSP-Advance and is considered a stand-alone improvement to the state-of-the-art transit signal priority. Phase three expands the study further by developing an optimization based system for integrated traffic and transit control (ITRAC). ITRAC extends the optimization module in phase one to explicitly include transit delay. ITRAC benefits from insights gained in phase two, but actually replaces TSP-Advance with an extended optimization procedure. It is recommended that TSP-Advance would be deployed if a traffic signal control system is already in place. If a complete integrated solution is sought, then ITRAC would be the recommended approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".