A Comprehensive Analysis of the Impacts of Transit Driver Advisory Systems with Space Priority on the Corridor Performance
Bibliographic record
Abstract
Connected vehicles open the way for offering new strategies that can prioritize transit vehicles without having adverse impacts on the general traffic. Driver advisory system (DAS) is one strategy that relies on vehicle-to-infrastructure communication to advise bus drivers with specific traveling speeds and dwell times that achieve specific objectives. In this study, we conducted a comprehensive analysis of the impacts of different DAS algorithms on the performance of electric buses (e-buses) and the general traffic. Additionally, we examined two supporting space priority strategies to allow the buses to travel at the advised speeds. A simulation model for the Eglinton East corridor in Toronto, Canada was built using Aimsun Next in order to quantify the impacts of the DAS algorithms. The results show that DAS is a promising strategy that allows the buses to travel near the maximum possible speed with 25% to 55% reduction in the total number of stops, 20% to 55% higher levels of comfort, and 10% to 20% reduction in the bus energy consumption rates. Additionally, the DAS can improve the headway regularity, improving the level of service (LOS) from LOS F to LOS C when the headway regularity was added to the objectives of the DAS.
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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 | 0.003 |
| 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 | 0.000 |
| 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".