An efficient transit signal priority (TSP) in connected vehicle (CV) Environment: A dynamic bargaining-game framework with a stochastic shockwave profile model

• 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.