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 applications. This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications, representative journals/conferences, and leading topics in recent 10 years. Then, this paper presents a comprehensive literature review on applications of RL in transportation based on specific topics. The potential future research directions of RL applications and developments are also discussed.