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Transportation is the backbone of the economy and urban development.Improving the efficiency, sustainability, resilience, and intelligence oftransportation systems is critical and also challenging. The constantlychanging traffic conditions, the uncertain influence of external factors (e.g.,weather, accidents), and the interactions among multiple travel modes andmulti-type flows result in the dynamic and stochastic natures of transportationsystems. The planning, operation, and control of transportation systems requireflexible and adaptable strategies in order to deal with uncertainty,non-linearity, variability, and high complexity. In this context, ReinforcementLearning (RL) that enables autonomous decision-makers to interact with thecomplex environment, learn from the experiences, and select optimal actions hasbeen rapidly emerging as one of the most useful approaches for smarttransportation. This paper conducts a bibliometric analysis to identify thedevelopment of RL-based methods for transportation applications, typicaljournals/conferences, and leading topics in the field of intelligenttransportation in recent ten years. Then, this paper presents a comprehensiveliterature review on applications of RL in transportation by categorizingdifferent methods with respect to the specific application domains. Thepotential future research directions of RL applications and developments arealso discussed.

A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications