Latent factor model for traffic signal control

The increased ownership of motor vehicles has brought many urban problems, such as traffic congestion, environmental pollution. Traffic signal control is recognized as one of effective ways to alleviate these problems. However, it is still hard to automatically choose appropriate traffic signal timing plans for different traffic conditions due to the dynamics and uncertainty of transportation systems. In this paper, we propose a latent factor model based traffic signal timing plan recommendation method to address this problem. In the proposed method, we model the abstract traffic states as the “users” in recommendation systems, and timing plans as the “items”. And there are many explicit or implicit factors in the interactions between “users” and “items”. The latent factor model is successfully used to deal with uncertain factors which cannot be modeled accurately in math. The novel method adopted the model-free adaptive idea to solve the problem of modeling from the perspective of data mining and machine learning framework. And, the proposed method is tested by using simulation data generated by a microscopic traffic simulator called Paramics. The results are compared to the baseline Webster method. The results indicate that the proposed latent factor model based recommendation method outperforms the Webster method on reducing the delay.