基于人工智能的智能交通系统优化研究

基于人工智能的智能交通系统优化研究

赵 熙

（天津滨海新区城市规划设计研究院有限公司，天津 300451）

【摘 要】随着城市化进程的加快和交通需求的增加，传统的交通管理系统面临着日益严重的挑战，亟需通过先进技术手段实现智能化转型。本文深入探讨了人工智能（Artificial Intelligence, AI）在智能交通系统（Intelligent Transportation Systems, ITS）中的应用，重点分析了 AI 技术在交通流优化、信号控制及公共交通调度中的具体实施路径。通过对 AI 技术现状的概述，明确了 ITS 的发展历程及其重要性，提出了 AI 在交通系统中的多种优化策略，包括路径规划优化、交通信号 控制优化和公共交通调度优化，揭示了这些方法如何有效提升交通系统的整体性能和运作效率。同时，本文还通过数据分 析与预测、算法优化及实际案例分析的框架，系统研究了交通数据的采集与解析、交通流的前瞻性预测及用户行为的建模 等诸多方面。其中，遗传算法（Genetic Algorithm, GA）、粒子群算法（Particle Swarm Optimization, PSO）和神经网络（Neural Networks, NN）的优化应用被详细探讨，展现了它们在解决复杂交通问题中的有效性和可扩展性。结合实际案例，本文最终提出了未来 ITS 与 AI 结合的研究方向，为交通领域的智能化进步提供了理论支持和实践指导，促进智慧城市的建设目标的实现。通过这一系列研究，本文明确了 AI 在智能交通系统优化中的核心作用，期望为交通管理者、政策制定者及研究人员提供有价值的参考依据。

【关键词】智能交通系统；人工智能技术；交通流量优化；路径规划；交通信号控制；数据分析

Research on the Optimization of Intelligent Transportation Systems Based on Artificial Intelligence

Xi Zhao

(Tianjin Binhai New Area Urban Planning and Design Institute Co., Ltd., Tianjin 300451)

Abstract: With the acceleration of urbanization and the increase in transportation demand, traditional traffic management systems are facing increasingly severe challenges and are in urgent need of intelligent transformation through advanced technological means. This paper deeply explores the application of Artificial Intelligence (AI) in Intelligent Transportation Systems (ITS), focusing on the specific implementation paths of AI technology in traffic flow optimization, signal control, and public transport scheduling. By providing an overview of the current state of AI technology, the paper clarifies the development history and importance of ITS and proposes various optimization strategies of AI in the traffic system, including route planning optimization, traffic signal control optimization, and public transport scheduling optimization, revealing how these methods effectively enhance the overall performance and operational efficiency of the traffic system. At the same time, this paper systematically studies many aspects such as traffic data collection and parsing, forward-looking prediction of traffic flow, and user behavior modeling through a framework of data analysis and prediction, algorithm optimization, and actual case analysis. The optimization applications of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Neural Networks (NN) are discussed in detail, demonstrating their effectiveness and scalability in solving complex traffic problems. Combined with practical cases, this paper ultimately proposes future research directions for the integration of ITS and AI, providing theoretical support and practical guidance for the intelligent progress in the field of transportation and promoting the realization of smart city construction goals. Through this series of studies, this paper clarifies the core role of AI in the optimization of intelligent transportation systems, hoping to provide valuable reference for traffic managers, policymakers, and researchers.

Keywords: Intelligent Transportation Systems; Artificial Intelligence Technology; Traffic Flow Optimization; Route Planning; Traffic Signal Control; Data Analysis