Research on Expressway Congestion Warning Technology Based on YOLOv11-DIoU and GRU-Attention
Tong Yulin, Liang Xuechen
TL;DR
This work tackles expressway congestion by integrating enhanced perception with a time-aware congestion predictor. It improves vehicle detection via YOLOv11-DIoU (DIoU loss) and tracking via DeepSort (fusion of motion and appearance features), achieving $mAP = 95.7\%$ and $MOTA = 93.8\%$, while establishing a strong negative speed-density relationship of $r = -0.97$. For forecasting, a GRU-Attention model captures key congestion precursors, reaching $Accuracy = 99.7\%$ and $RMSE = 0.05$, and delivering early warnings with errors ≤ $1$ minute for 10-minute-ahead alerts; full-video testing yields 95% early-warning accuracy and over 90% spatial overlap. The framework offers a quantitative, deployable solution for expressway congestion control and intelligent transportation systems.
Abstract
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.