  Deep reinforcement learning (DRL) is an emerging methodology that is
transforming the way many complicated transportation decision-making problems
are tackled. Researchers have been increasingly turning to this powerful
learning-based methodology to solve challenging problems across transportation
fields. While many promising applications have been reported in the literature,
there remains a lack of comprehensive synthesis of the many DRL algorithms and
their uses and adaptations. The objective of this paper is to fill this gap by
conducting a comprehensive, synthesized review of DRL applications in
transportation. We start by offering an overview of the DRL mathematical
background, popular and promising DRL algorithms, and some highly effective DRL
extensions. Building on this overview, a systematic investigation of about 150
DRL studies that have appeared in the transportation literature, divided into
seven different categories, is performed. Building on this review, we continue
to examine the applicability, strengths, shortcomings, and common and
application-specific issues of DRL techniques with regard to their applications
in transportation. In the end, we recommend directions for future research and
present available resources for actually implementing DRL.
