  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-world scenarios. Because of the high
complexity of the optimisation problem for modelling the traffic, experimental
settings of existing 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 solutions. To address these
issues, in this work we carefully design our settings and propose a new dataset
including both synthetic and real traffic data in more complex scenarios.
Additionally, we propose a novel baseline model with strong performance. It is
based on deep reinforcement learning with an encoder-decoder structure: an
edge-weighted graph convolutional encoder to excavate multi-intersection
relations; and an 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
the multi-intersection traffic optimisation problem. Models are trained/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 multiple competitive methods.
