Online Adaptive Traffic Signal Coordination with a Game Theoretic Approach
MetadataShow full item record
In the coming era of Connected and Automated Vehicles (CAV), there is a pressing need to develop online adaptive traffic signal control algorithms given rich real-time data from CAVs. The occurrence of CAV technologies brings an opportunity to develop a real-time data-driven signal coordination method which cooperatively optimize the network performance at a network level. This thesis proposes a game theoretic approach, epsilon-equilibrium algorithm, to achieve online adaptive coordination. Each intersection of the traffic network is just like a player in a game. Different intersections pursue their own benefit maximization. This thesis also compares the network delay performances between CAVs and Human-driven Vehicle (HDV). A simulation platform is built using Matlab to code and evaluate the proposed algorithms and models. The effect, applicability and efficiency of the game theoretic approach in signal coordination are examined. The game theoretical approach is proven to outperform the systematical optimization on vehicle delay at intersection level in terms of delay equity. The variances of vehicle delay among different intersections are significantly decreased by the proposed game theoretic algorithm. Thus no intersection needs to sacrifice its own delay performance to achieve system optimal. The results also show that the CAVs can achieve better delay performances compared to HDVs.