An online adaptive learning approach for predicting multi-type traffic participants' microscopic behavior

Area of Science:

• Intelligent Transportation Systems
• Deep Learning for Behavioral Prediction
• Traffic Flow Dynamics

Background:

• Predicting multi-type traffic participant behavior in dynamic transportation hubs is complex.
• Current deep learning models struggle with online learning, error correction, and cross-scenario generalization.
• Prediction errors often persist due to a lack of real-time correction mechanisms.

Purpose of the Study:

• To develop an adaptive framework for reliable, real-time prediction of traffic participant behavior.
• To overcome limitations of existing models in online learning, error correction, and generalization.
• To enhance the prediction accuracy and reliability in dynamic transportation environments.

Main Methods:

• An adaptive framework integrating online learning with probabilistic error correction.
• Utilized an Extended Kalman Filter for real-time trajectory correction.
• Employed a hierarchical graph encoder for efficient transfer learning and unified node-edge-plane modeling for multimodal context fusion.

Main Results:

• The proposed method significantly outperforms existing approaches in predicting behavior in unseen scenarios.
• Demonstrated robust performance using real-world transportation hub data.
• Achieved superior cross-scenario generalization with minimal retraining requirements.

Conclusions:

• The adaptive framework offers a promising solution for real-time behavioral prediction in modern traffic systems.
• The integration of online learning and probabilistic error correction enhances model reliability.
• The approach effectively addresses the challenges of dynamic environments and diverse traffic participant behaviors.