基于强化学习的自适应多尺度时空卷积网络的车道级交通流动态预测

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2025-09-14 DOI:10.1016/j.array.2025.100513
Xiaohui Yang , Shaowei Sun , Mingzhou Liu
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引用次数: 0

摘要

针对复杂城市环境中微观驾驶行为的突变和时空依赖性带来的挑战,提出了一种自适应多尺度时空卷积网络和强化学习协同优化车道级交通流预测模型(AST-RLM)。该模型通过动态图构建机制、基于异构感知的多尺度卷积网络和基于dqn的协同优化框架实现了高精度车道级交通流预测。实验结果表明,AST-RLM在包含超过10,000条车道的多个城市的真实数据集上表现出色。晚峰值期间的平均绝对误差(MAE)低至0.033,与GraphWaveNet相比降低了38.9%。30分钟预测的均方根误差(RMSE)为3.98,优于ST-MetaNet等现有模型,即使在极端天气条件下,该模型也保持92.4%的稳定性。值得注意的是,在交通事故等突发事件中,动态图模块实时适应拓扑变化,预测误差降低26.7% - 30.9%,显著提高了模型在复杂动态场景下的鲁棒性和响应性。此外,AST-RLM在边缘设备上的多智能体强化学习部署实现了比GC-RL快3.6倍的收敛速度,验证了其在现实交通系统中的效率和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive multi-scale spatio-temporal convolutional network with reinforcement learning for dynamic lane-level traffic flow prediction
This paper presents an Adaptive Multi-Scale Spatio-Temporal Convolutional Network and Reinforcement Learning Collaborative Optimization Lane-Level Traffic Flow Prediction Model (AST-RLM), designed to address the challenges posed by the sudden changes in microscopic driving behaviors and spatio-temporal dependencies in complex urban environments. The model achieves high-precision lane-level traffic flow prediction through dynamic graph construction mechanisms, heterogeneous perception-based multi-scale convolutional networks, and a DQN-based collaborative optimization framework. Experimental results demonstrate that AST-RLM performs exceptionally well on real-world datasets from multiple cities, containing over 10,000 lanes. The average absolute error (MAE) during the evening peak is as low as 0.033, a 38.9 % reduction compared to GraphWaveNet. The root mean square error (RMSE) for 30-min predictions is 3.98, outperforming existing models like ST-MetaNet, and the model maintains 92.4 % stability even in extreme weather conditions. Notably, during sudden events like traffic accidents, the dynamic graph module adapts in real-time to changes in topology, reducing prediction errors by 26.7 %–30.9 %, significantly improving the model's robustness and responsiveness in complex dynamic scenarios. Furthermore, AST-RLM's multi-agent reinforcement learning deployment on edge devices achieves a convergence speed 3.6 times faster than GC-RL, validating its efficiency and feasibility in real-world traffic systems.
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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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