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This study introduces a Spatio-Temporal Decoupled Autoencoder that effectively predicts ramp traffic flows using only mainline data, eliminating the need for scarce real-time ramp detectors. By leveraging cross-modal reconstruction pretraining, the framework captures intrinsic spatio-temporal relationships and consistently outperforms thirteen state-of-the-art baselines across three real-world datasets. The approach offers a plug-and-play solution for diverse forecasting pipelines, demonstrating significant potential for improving traffic management in infrastructure-constrained environments.
Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges
arXiv:2510.03381v3 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2510.03381 [cs.LG]
(or arXiv:2510.03381v3 [cs.LG] for this version)
https://doi.org/10.1016/j.asoc.2026.115462 DOI(s) linking to related resources
From: Yongchao Li Ph.D. candidate [view email] [v1] Fri, 3 Oct 2025 15:26:56 UTC (5,987 KB) [v2] Thu, 27 Nov 2025 14:40:09 UTC (5,274 KB) [v3] Fri, 5 Jun 2026 07:39:08 UTC (5,313 KB)
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