A bibliometric analysis and review on reinforcement learning for transportation applications

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.

Automated summary

Transportation is crucial for the economy and urban development, but it faces challenges in terms of efficiency, sustainability, resilience, and intelligence. Reinforcement Learning (RL) has emerged as a useful approach for smart transportation applications, allowing autonomous decision-makers to learn from experiences and make optimal actions in complex environments. This paper conducts a bibliometric analysis to understand the development of RL-based methods in transportation applications and provides a comprehensive literature review on the specific topics. Future research directions for RL applications and developments are also discussed.