Deep Imitation Learning for Traffic Signal Control and Operations Based on Graph Convolutional Neural Networks

Traffic signal control plays an essential role in the Intelligent Transportation Systems (ITS). Due to the intrinsic uncertainty and the significant increase in travel demand, in many cases, a traffic system still has to rely on human engineers to cope with the complicated and challenging traffic control and operation problem, which cannot be handled well by the traditional methods alone. Thus, imitating the good working experience of engineers to solve traffic signal control problems remains a practical, smart, and cost effective approach. In this paper, we construct a modelling framework to imitate how engineers cope with complex scenarios through learning from the historical record of manipulations by traffic operators. To extract spatial-temporal traffic demand features of the entire road network, a specially designed mask and a graph convolutional neural network (GCNN) are employed in this framework. The simulation experiments results showed that, compared with the original deployed control scheme, our method reduced the average waiting time, average time loss of vehicles, and vehicle throughput by 6.6 %, 7.2%, and 6.85%, respectively.