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 extra
The increasing complexity of urban infrastructure and the drive for greater efficiency in transportation systems necessitate advanced predictive AI solutions.
Improved traffic prediction, especially in bottleneck areas like highway interchanges, can significantly enhance urban mobility, reduce congestion, and impact logistical efficiency.
The proposed STDAE framework offers a new method to overcome data blind spots in real-time traffic prediction by leveraging pre-training on existing data sources.
- · Smart city initiatives
- · Logistics and transportation companies
- · Urban planners
- · AI/ML developers
- · Cities with outdated traffic management systems
- · Commuters in poorly optimized traffic environments
Ramp flow prediction becomes more accurate, leading to better traffic flow management.
Reduced urban congestion could free up economic capacity and decrease commute times for millions.
Widely deployed, such systems could form a foundational layer for autonomous vehicle navigation and large-scale urban simulations.
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Read at arXiv cs.LG