  Transportation is the backbone of the economy and urban development.
Improving the efficiency, sustainability, resilience, and intelligence of
transportation systems is critical and also challenging. The constantly
changing traffic conditions, the uncertain influence of external factors (e.g.,
weather, accidents), and the interactions among multiple travel modes and
multi-type flows result in the dynamic and stochastic natures of transportation
systems. The planning, operation, and control of transportation systems require
flexible and adaptable strategies in order to deal with uncertainty,
non-linearity, variability, and high complexity. In this context, Reinforcement
Learning (RL) that enables autonomous decision-makers to interact with the
complex environment, learn from the experiences, and select optimal actions has
been rapidly emerging as one of the most useful approaches for smart
transportation. This paper conducts a bibliometric analysis to identify the
development of RL-based methods for transportation applications, typical
journals/conferences, and leading topics in the field of intelligent
transportation in recent ten years. Then, this paper presents a comprehensive
literature review on applications of RL in transportation by categorizing
different methods with respect to the specific application domains. The
potential future research directions of RL applications and developments are
also discussed.
