{"title":"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","authors":"Xiaohui Yang , Shaowei Sun , Mingzhou Liu","doi":"10.1016/j.array.2025.100513","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"28 ","pages":"Article 100513"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive multi-scale spatio-temporal convolutional network with reinforcement learning for dynamic lane-level traffic flow prediction\",\"authors\":\"Xiaohui Yang , Shaowei Sun , Mingzhou Liu\",\"doi\":\"10.1016/j.array.2025.100513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":8417,\"journal\":{\"name\":\"Array\",\"volume\":\"28 \",\"pages\":\"Article 100513\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Array\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590005625001407\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Array","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590005625001407","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
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.