用改进模型提升拥堵感知与预测精度，实现提前10分钟精准预警。
Research on Expressway Congestion Warning Technology Based on YOLOv11-DIoU and GRU-Attention
- 融合DIoU损失与多距离追踪，提升遮挡下车辆检测与跟踪准确率。
- GRU-Attention模型预测准确率达99.7%，提前10分钟预警误差小于1分钟。
- 适合智慧交通系统部署，尤其适用于高流量路段的实时拥堵管理。
高速公路拥堵严重降低通行效率并影响区域连通性。现有“检测-预测”系统存在遮挡下感知不准、长期依赖关系丢失等问题。本研究提出一体化技术框架：在交通流感知方面，将YOLOv11替换为YOLOv11-DIoU（DIoU Loss替代GIoU Loss），DeepSort融合马氏距离与余弦距离；在昌深高速视频上，该方法达到95.7% mAP（较基线提升6.5个百分点），遮挡漏检率仅5.3%；DeepSort实现93.8% MOTA（较SORT提升11.3个百分点），仅4次身份切换。基于Greenberg模型（10-15辆/km高密度场景），速度与密度呈强负相关（r=-0.97），符合交通流理论。在拥堵预警方面，构建GRU-Attention模型，基于流量、密度、速度训练300轮，测试准确率达99.7%（较传统GRU高7-9个百分点）；针对30分钟拥堵事件进行10分钟提前预警，时间误差≤1分钟。独立视频验证显示预警准确率95%，拥堵区域空间重合度超90%，在＞5辆/秒高流量场景下性能稳定。该框架为高速公路拥堵管控提供量化支持，具备智能交通应用前景。
原文摘要 · Abstract (English)
Expressway traffic congestion severely reduces travel efficiency and hinders regional connectivity. Existing "detection-prediction" systems have critical flaws: low vehicle perception accuracy under occlusion and loss of long-sequence dependencies in congestion forecasting. This study proposes an integrated technical framework to resolve these issues.For traffic flow perception, two baseline algorithms were optimized. Traditional YOLOv11 was upgraded to YOLOv11-DIoU by replacing GIoU Loss with DIoU Loss, and DeepSort was improved by fusing Mahalanobis (motion) and cosine (appearance) distances. Experiments on Chang-Shen Expressway videos showed YOLOv11-DIoU achieved 95.7\% mAP (6.5 percentage points higher than baseline) with 5.3\% occlusion miss rate. DeepSort reached 93.8\% MOTA (11.3 percentage points higher than SORT) with only 4 ID switches. Using the Greenberg model (for 10-15 vehicles/km high-density scenarios), speed and density showed a strong negative correlation (r=-0.97), conforming to traffic flow theory. For congestion warning, a GRU-Attention model was built to capture congestion precursors. Trained 300 epochs with flow, density, and speed, it achieved 99.7\% test accuracy (7-9 percentage points higher than traditional GRU). In 10-minute advance warnings for 30-minute congestion, time error was $\leq$ 1 minute. Validation with an independent video showed 95\% warning accuracy, over 90\% spatial overlap of congestion points, and stable performance in high-flow ($>$5 vehicles/second) scenarios.This framework provides quantitative support for expressway congestion control, with promising intelligent transportation applications.
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