通过分层分解预测路口转向流量，提升精度并适配实时控制。
Hierarchical Flow Decomposition for Turning Movement Prediction at Signalized Intersections
- 先预测主干道直行流，再分解到具体转向流，符合交通实际结构。
- 15分钟间隔预测平均误差仅2.49辆，比Transformer低5.7%，比GRU低27%。
- 物理约束损失函数保证流量守恒，训练速度是DCRNN的12.8倍，适合实时应用。
准确预测信号交叉口的转向流量对自适应信号控制至关重要，但受方向性流量高波动性影响仍具挑战。本文提出HFD-TM（分层流量分解转向流量预测模型），通过先预测路段直行流量，再将其扩展至各转向流，利用实证交通结构：路段流量占总流量65.1%，波动性更低，且解释了35.5%的转向流量方差。引入物理信息损失函数以确保流量守恒。在纳什维尔六路口路段为期六个月、每15分钟一次的LiDAR数据上评估，HFD-TM实现每区间2.49辆的平均绝对误差，相比Transformer降低5.7%，相比GRU降低27.0%。消融实验表明分层分解带来最大性能提升，训练时间仅为DCRNN的12.8倍，适用于实时交通场景。
原文摘要 · Abstract (English)
Accurate prediction of intersection turning movements is essential for adaptive signal control but remains difficult due to the high volatility of directional flows. This study proposes HFD-TM (Hierarchical Flow-Decomposition for Turning Movement Prediction), a hierarchical deep learning framework that predicts turning movements by first forecasting corridor through-movements and then expanding these predictions to individual turning streams. This design is motivated by empirical traffic structure, where corridor flows account for 65.1% of total volume, exhibit lower volatility than turning movements, and explain 35.5% of turning-movement variance. A physics-informed loss function enforces flow conservation to maintain structural consistency. Evaluated on six months of 15-minute interval LiDAR (Light Detection and Ranging) data from a six-intersection corridor in Nashville, Tennessee, HFD-TM achieves a mean absolute error of 2.49 vehicles per interval, reducing MAE by 5.7% compared to a Transformer and by 27.0% compared to a GRU (Gated Recurrent Unit). Ablation results show that hierarchical decomposition provides the largest performance gain, while training time is 12.8 times lower than DCRNN (Diffusion Convolutional Recurrent Neural Network), demonstrating suitability for real-time traffic applications.
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