  Traffic signal control is an important and challenging real-world problem,
which aims to minimize the travel time of vehicles by coordinating their
movements at the road intersections. Current traffic signal control systems in
use still rely heavily on oversimplified information and rule-based methods,
although we now have richer data, more computing power and advanced methods to
drive the development of intelligent transportation. With the growing interest
in intelligent transportation using machine learning methods like reinforcement
learning, this survey covers the widely acknowledged transportation approaches
and a comprehensive list of recent literature on reinforcement for traffic
signal control. We hope this survey can foster interdisciplinary research on
this important topic.
