基于YOLO-LSTM模型的高速公路车道有效利用方法
Xing Tong1, Anxiang Huang1, Yunxiao Pan1
1College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了一种使用YOLOv8n和DeepSORT进行实时交通分析的AI模型,成功实施了应急车道系统,以减少高速公路拥堵并改善交通流动.
科学领域:
- 计算机科学 计算机科学
- 土木工程 土木工程是指土木工程.
- 运输科学 运输科学
背景情况:
- 城市发展加剧了交通拥堵,降低了高速公路运输效率.
- 实时交通管理对于缓解拥堵和优化道路网络至关重要.
研究的目的:
- 开发和验证用于实时交通拥堵分类和管理的智能系统.
- 为优化高速公路应急车道使用提供数据驱动的方法.
主要方法:
- 一个改进的ML-YOLO (YOLOv8n) 视频数据提取模型与DeepSORT集成用于对象跟踪.
- 实时提取交通流量参数以使用K-means集群对拥堵水平进行分类.
- 实施长期短期记忆丢失 (LSTM-Dropout) 和紧急通道开放模型用于拥堵警告.
主要成果:
- 该模型准确地对交通拥堵水平进行分类,并提供实时流量参数提取.
- 该系统成功实施了道路拥堵警告和紧急车道管理策略.
- 通过LSTM时间序列模型进行验证,相对误差较低,证实了模型的稳定性和实用性.
结论:
- 开发的模型为高速公路交通管理人员提供了一种可靠的方法,以决定紧急车道操作.
- 该系统有效地缓解了高速公路拥堵,提高了交通效率,并具有显著的实际价值.
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