摘要
随着城市化进程的加快和机动车数量的持续增长，交通拥堵问题日益严重，智能交通系统作为缓解这一问题的重要手段受到广泛关注，其中车辆路径规划算法是核心组成部分之一本研究旨在提出一种基于改进蚁群优化算法的车辆路径规划方法，以提高路径规划效率和适应动态交通环境通过引入自适应参数调整机制和局部搜索策略，算法能够更有效地平衡全局搜索能力和局部优化能力实验采用真实交通数据集进行验证，结果表明，所提算法在寻找最优路径方面较传统蚁群算法和遗传算法具有显著优势，特别是在复杂动态场景下表现出更强的鲁棒性和实时性此外，该算法还结合了交通预测模型，可提前规避潜在拥堵区域，从而进一步提升路径规划的合理性与实用性本研究的主要贡献在于提出了一种适用于动态交通环境的高效路径规划算法，为智能交通系统的实际应用提供了新的解决方案，同时为未来相关研究奠定了理论基础
关键词：智能交通系统；车辆路径规划；改进蚁群优化算法
Abstract
With the accelerating urbanization process and the continuous increase in the number of motor vehicles, traffic congestion has become an increasingly severe issue. Intelligent transportation systems (ITS) have garnered significant attention as a critical solution to this problem, with vehicle routing planning algorithms serving as one of the core components. This study proposes an improved ant colony optimization (ACO)-based algorithm for vehicle routing planning to enhance path planning efficiency and adaptability in dynamic traffic environments. By incorporating an adaptive parameter adjustment mechanism and local search strategies, the algorithm effectively balances global exploration and local exploitation capabilities. The experimental validation was conducted using real-world traffic datasets, and the results demonstrate that the proposed algorithm outperforms traditional ACO and genetic algorithms in finding optimal paths, particularly exhibiting stronger robustness and real-time performance in complex dynamic scenarios. Additionally, the algorithm integrates a traffic prediction model to preemptively avoid potential congestion areas, thereby further improving the rationality and practicality of path planning. The primary contribution of this research lies in proposing an efficient routing planning algorithm tailored for dynamic traffic environments, providing a novel solution for the practical application of intelligent transportation systems while laying a theoretical foundation for future related studies.
Keywords：Intelligent Transportation System; Vehicle Routing Planning; Improved Ant Colony Optimization Algorithm
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能交通系统与路径规划背景 1
(二) 车辆路径规划算法研究现状 1
(三) 本文研究方法与技术路线 2
二、车辆路径规划算法基础理论 2
(一) 智能交通系统的基本框架 2
(二) 路径规划的核心数学模型 3
(三) 常见路径规划算法分类与特点 3
三、动态环境下的路径规划优化 4
(一) 动态交通数据的获取与处理 4
(二) 实时路径规划算法设计 5
(三) 算法性能评估指标体系 5
四、多目标路径规划算法研究 6
(一) 多目标优化问题定义 6
(二) 权衡时间与能耗的路径规划 7
(三) 算法在复杂场景中的应用分析 7
结 论 9
参考文献 10
随着城市化进程的加快和机动车数量的持续增长，交通拥堵问题日益严重，智能交通系统作为缓解这一问题的重要手段受到广泛关注，其中车辆路径规划算法是核心组成部分之一本研究旨在提出一种基于改进蚁群优化算法的车辆路径规划方法，以提高路径规划效率和适应动态交通环境通过引入自适应参数调整机制和局部搜索策略，算法能够更有效地平衡全局搜索能力和局部优化能力实验采用真实交通数据集进行验证，结果表明，所提算法在寻找最优路径方面较传统蚁群算法和遗传算法具有显著优势，特别是在复杂动态场景下表现出更强的鲁棒性和实时性此外，该算法还结合了交通预测模型，可提前规避潜在拥堵区域，从而进一步提升路径规划的合理性与实用性本研究的主要贡献在于提出了一种适用于动态交通环境的高效路径规划算法，为智能交通系统的实际应用提供了新的解决方案，同时为未来相关研究奠定了理论基础
关键词：智能交通系统；车辆路径规划；改进蚁群优化算法
Abstract
With the accelerating urbanization process and the continuous increase in the number of motor vehicles, traffic congestion has become an increasingly severe issue. Intelligent transportation systems (ITS) have garnered significant attention as a critical solution to this problem, with vehicle routing planning algorithms serving as one of the core components. This study proposes an improved ant colony optimization (ACO)-based algorithm for vehicle routing planning to enhance path planning efficiency and adaptability in dynamic traffic environments. By incorporating an adaptive parameter adjustment mechanism and local search strategies, the algorithm effectively balances global exploration and local exploitation capabilities. The experimental validation was conducted using real-world traffic datasets, and the results demonstrate that the proposed algorithm outperforms traditional ACO and genetic algorithms in finding optimal paths, particularly exhibiting stronger robustness and real-time performance in complex dynamic scenarios. Additionally, the algorithm integrates a traffic prediction model to preemptively avoid potential congestion areas, thereby further improving the rationality and practicality of path planning. The primary contribution of this research lies in proposing an efficient routing planning algorithm tailored for dynamic traffic environments, providing a novel solution for the practical application of intelligent transportation systems while laying a theoretical foundation for future related studies.
Keywords：Intelligent Transportation System; Vehicle Routing Planning; Improved Ant Colony Optimization Algorithm
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能交通系统与路径规划背景 1
(二) 车辆路径规划算法研究现状 1
(三) 本文研究方法与技术路线 2
二、车辆路径规划算法基础理论 2
(一) 智能交通系统的基本框架 2
(二) 路径规划的核心数学模型 3
(三) 常见路径规划算法分类与特点 3
三、动态环境下的路径规划优化 4
(一) 动态交通数据的获取与处理 4
(二) 实时路径规划算法设计 5
(三) 算法性能评估指标体系 5
四、多目标路径规划算法研究 6
(一) 多目标优化问题定义 6
(二) 权衡时间与能耗的路径规划 7
(三) 算法在复杂场景中的应用分析 7
结 论 9
参考文献 10
