  Sub-optimal control policies in transportation systems negatively impact
mobility, the environment and human health. Developing optimal transportation
control systems at the appropriate scale can be difficult as cities'
transportation systems can be large, complex and stochastic. Intersection
traffic signal controllers are an important element of modern transportation
infrastructure where sub-optimal control policies can incur high costs to many
users. Many adaptive traffic signal controllers have been proposed by the
community but research is lacking regarding their relative performance
difference - which adaptive traffic signal controller is best remains an open
question. This research contributes a framework for developing and evaluating
different adaptive traffic signal controller models in simulation - both
learning and non-learning - and demonstrates its capabilities. The framework is
used to first, investigate the performance variance of the modelled adaptive
traffic signal controllers with respect to their hyperparameters and second,
analyze the performance differences between controllers with optimal
hyperparameters. The proposed framework contains implementations of some of the
most popular adaptive traffic signal controllers from the literature;
Webster's, Max-pressure and Self-Organizing Traffic Lights, along with deep
Q-network and deep deterministic policy gradient reinforcement learning
controllers. This framework will aid researchers by accelerating their work
from a common starting point, allowing them to generate results faster with
less effort. All framework source code is available at
https://github.com/docwza/sumolights.
