  The control of traffic signals is fundamental and critical to alleviate
traffic congestion in urban areas. However, it is challenging since traffic
dynamics are complicated in real situations. Because of the high complexity of
modelling the optimisation problem, experimental settings of current works are
often inconsistent. Moreover, it is not trivial to control multiple
intersections properly in real complex traffic scenarios due to its vast state
and action space. Failing to take intersection topology relations into account
also results in inferior traffic condition. To address these issues, in this
work we carefully design our settings and propose new data including both
synthetic and real traffic data in more complex scenarios. Additionally, we
propose a novel and strong baseline model based on deep reinforcement learning
with the encoder-decoder structure: an edge-weighted graph convolutional
encoder to excavate multi-intersection relations; and a unified structure
decoder to jointly model multiple junctions in a comprehensive manner, which
significantly reduces the number of the model parameters. By doing so, the
proposed model is able to effectively deal with multi-intersection traffic
optimisation problems. Models have been trained and tested on both synthetic
and real maps and traffic data with the Simulation of Urban Mobility (SUMO)
simulator. Experimental results show that the proposed model surpasses existing
methods in the literature.
