An efficient transit signal priority (TSP) in connected vehicle (CV) Environment: A dynamic bargaining-game framework with a stochastic shockwave profile model
Bibliographic record
Abstract
• A new TSP approach in connected vehicle environment is presented. • A stochastic shockwave profile is used to capture the stochasticity in traffic condition. • A bargaining game is designed to enhance cooperative decision-making and efficiency. • Heterogeneous passenger types are considered to enable a transit user-centered TSP. • TSP logic is evaluated from the perspectives of efficiency, reliability, robustness, and equity. This paper introduces a novel transit signal priority (TSP) strategy in a connected vehicle (CV) environment. While leveraging a green reallocation scheme, the proposed TSP strategy employs a stochastic shockwave profile model (SPM) to capture the dynamic interplay between fluctuating demand and supply. SPM treats inflow rate and link exit capacity as stochastic processes, incorporating the mixed traffic stream consisting of transit and private vehicles and their interactions. A bargaining-game framework is formulated, where each signal phase is a player in the game, receiving information from other players to bargain and solve a local optimization problem. A key feature of our game-theoretic approach is the promotion of cooperative decision-making among players with conflicting objectives to enhance green time reallocation strategies. This cooperation aims for a balanced distribution of breakdown occurrences without much compromising system-wide efficiency, ensuring equitable treatment of all intersection users, including boarding passengers, those waiting at transit stops, and private car users. In addition, the TSP problem is formulated to incorporate heterogeneous passenger types, allowing for a more accurate representation of transit users and creating a transit user-centered TSP. The performance of the developed model was evaluated through a case study at a busy intersection in downtown Calgary, focusing on performance indicators such as efficiency, reliability, robustness, and equitable treatment of the conflicting movements. The results show that the bargaining SPM-based TSP model achieves comparable or superior performance to the benchmark models across several key evaluation criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".