论文标题
关于运输应用的强化学习的文献计量分析和审查
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications
论文作者
论文摘要
运输是经济和城市发展的骨干。提高运输系统的效率，可持续性，韧性和智慧至关重要，而且具有挑战性。不断变化的交通状况，外部因素（例如天气，事故）的不确定影响以及多种旅行模式和多类型流量之间的相互作用导致运输系统的动态和随机性出现。运输系统的计划，操作和控制需要灵活和适应性的策略，以应对不确定性，非线性，可变性和高复杂性。在这种情况下，使自主决策者能够与复杂环境互动，从经验中学习和精选的最佳动作的强化学习（RL）已迅速成为智能运输的最有用的方法之一。本文进行了文献计量分析，以确定用于运输应用，典型期刊/会议的基于RL的方法以及近十年来智能运输领域的主要主题。然后，本文通过对特定应用领域的不同方法进行分类，对RL在运输中的应用进行了全面的文献综述。还讨论了RL应用和发展的潜在研究方向。
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.
