Existing research on AI-based traffic management systems, utilizing techniques such as fuzzy logic, reinforcement learning, deep neural networks, and evolutionary algorithms, demonstrates the potential of AI to transform the traffic landscape. This article endeavors to review the topics where AI and traffic management intersect. It comprises areas like AI-powered traffic signal control systems, automatic distance and velocity recognition (for instance, in autonomous vehicles, hereafter AVs), smart parking systems, and Intelligent Traffic Management Systems (ITMS), which use data captured in real-time to keep track of traffic conditions, and traffic-related law enforcement and surveillance using AI. AI applications in traffic management cover a wide range of spheres. The spheres comprise, inter alia, streamlining traffic signal timings, predicting traffic bottlenecks in specific areas, detecting potential accidents and road hazards, managing incidents accurately, advancing public transportation systems, development of innovative driver assistance systems, and minimizing environmental impact through simplified routes and reduced emissions. The benefits of AI in traffic management are also diverse. They comprise improved management of traffic data, sounder route decision automation, easier and speedier identification and resolution of vehicular issues through monitoring the condition of individual vehicles, decreased traffic snarls and mishaps, superior resource utilization, alleviated stress of traffic management manpower, greater on-road safety, and better emergency response time.

翻译：现有研究利用模糊逻辑、强化学习、深度神经网络和进化算法等技术，展示了基于人工智能的交通管理系统变革交通格局的潜力。本文旨在综述人工智能与交通管理交叉领域的研究主题，涵盖人工智能驱动的交通信号控制系统、自动距离与速度识别（例如在自动驾驶车辆中，后文简称AVs）、智能停车系统，以及利用实时采集数据跟踪交通状况的智能交通管理系统（ITMS），还包括基于人工智能的交通执法与监控。人工智能在交通管理中的应用覆盖广泛领域，主要包括：优化交通信号配时、预测特定区域交通瓶颈、检测潜在事故与道路危险、精准管理突发事件、改进公共交通系统、开发创新驾驶辅助系统，以及通过简化路线和减少排放降低环境影响。人工智能在交通管理中的优势亦呈现多样性，包括：提升交通数据管理效能、优化路径决策自动化、通过监测单车状态更便捷快速地识别与解决车辆问题、减少交通拥堵与事故、提高资源利用率、缓解交通管理人员工作压力、增强道路安全性，以及缩短应急响应时间。