  Latest technological improvements increased the quality of transportation.
New data-driven approaches bring out a new research direction for all
control-based systems, e.g., in transportation, robotics, IoT and power
systems. Combining data-driven applications with transportation systems plays a
key role in recent transportation applications. In this paper, the latest deep
reinforcement learning (RL) based traffic control applications are surveyed.
Specifically, traffic signal control (TSC) applications based on (deep) RL,
which have been studied extensively in the literature, are discussed in detail.
Different problem formulations, RL parameters, and simulation environments for
TSC are discussed comprehensively. In the literature, there are also several
autonomous driving applications studied with deep RL models. Our survey
extensively summarizes existing works in this field by categorizing them with
respect to application types, control models and studied algorithms. In the
end, we discuss the challenges and open questions regarding deep RL-based
transportation applications.
