In recent years, the exacerbation of traffic congestion has sparked widespread interest in the research on traffic signal control algorithms. Current studies indicate that methods based on deep reinforcement learning (DRL) exhibit promising performance in simulated environments. However, challenges persist in their practical application, including substantial requirements for data and computational resources, as well as difficulties in achieving coordination between intersections. To address these challenges, this study proposes a novel traffic signal control algorithm based on a contextual multi-armed bandit model. In contrast to conventional algorithms, the proposed algorithm achieves efficient coordination between intersections by extracting the main arteries from the road network. Moreover, it employs a contextual multi-armed bandit model to facilitate rapid and effective traffic signal control. Finally, through extensive experimentation on both real and synthetic datasets, the superiority of the proposed algorithm over previous algorithms is empirically demonstrated.