用主路数据重建匝道流量，解决高速互通口无实时检测器的问题。
Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges
- 分时空解耦自编码器从主路数据重构匝道流量
- 在三个真实数据集上超越13个基线模型，接近使用真实匝道数据的性能
- 可直接接入各类预测模型，适合缺传感器场景
互通口是高速公路间车辆转换的关键节点，但缺乏实时匝道检测器导致交通预测存在盲区。为此，我们提出时空解耦自编码器（STDAE），一种两阶段框架，利用跨模态重建预训练。第一阶段，STDAE从主路数据重构历史匝道流量，迫使模型捕捉内在时空关系；其并行设计的时空自编码器结构可高效提取异质特征。第二阶段，将学习到的表征与GWNet等模型结合以提升预测精度。在三个真实互通口数据集上的实验表明，STDAE-GWNET始终优于13个前沿基线模型，性能接近使用真实匝道数据的模型。这证明了其在缓解检测器稀缺问题上的有效性，且具备即插即用潜力，适用于多种预测流程。
原文摘要 · Abstract (English)
Interchanges are crucial nodes for vehicle transfers between highways, yet the lack of real-time ramp detectors creates blind spots in traffic prediction. To address this, we propose a Spatio-Temporal Decoupled Autoencoder (STDAE), a two-stage framework that leverages cross-modal reconstruction pretraining. In the first stage, STDAE reconstructs historical ramp flows from mainline data, forcing the model to capture intrinsic spatio-temporal relations. Its decoupled architecture with parallel spatial and temporal autoencoders efficiently extracts heterogeneous features. In the prediction stage, the learned representations are integrated with models such as GWNet to enhance accuracy. Experiments on three real-world interchange datasets show that STDAE-GWNET consistently outperforms thirteen state-of-the-art baselines and achieves performance comparable to models using historical ramp data. This demonstrates its effectiveness in overcoming detector scarcity and its plug-and-play potential for diverse forecasting pipelines.
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