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With the increase in computing power and the development of reinforcement learning, the efficiency of the transportation system have been improving. Data-driven reinforcement learning algorithms have strong self-learning capabilities and adaptability to complex transportation systems and play a key role in the development of intelligent transportation systems. This paper reviews the basic concept of reinforcement learning firstly, including the terminology and methods which are divided into three categories: value-based, policy-based, and actor-critic methods. Secondly, the application and development of reinforcement learning in traffic signal control are introduced in the single-point control and the joint control of the multi-agent system. Then, this paper describes the development in autonomous driving separately from path planning and driving behavior. In the end, this paper concludes the challenges and problems faced by reinforcement learning methods in intelligent transportation systems.

The phase-based image matching is effective for both iris and palm recognition tasks. Hence, we can expect that the approach may be useful for multimodal biometric system having palmprint and iris recognition capabilities. This paper investigates the fusion of palmprint and iris biometric at image level. A new image fusion algorithm named Baud limited image product (BLIP) especially for phase-based image matching is proposed. Based on this, a new multi-biometric fusion scheme at image level that combines BLIP and phase-based image matching is proposed. The effective region of iris and palm images are first extracted respectively, then they are fused into one small size image using BLIP, finally matched with the template using phase-based image matching to get a score. The experimental results show that this new scheme can not only improve the system accuracy performance, but also reduce the memory size used to store the template and time consumed by the matching.