Transfer Learning in Traffic Engineering

We devote to designing transferable and self-adaptive models to handle the dynamic changes and data insufficient problems in short-term traffic prediction. Both model and sample based transfer strategies are investigated to reveal the rules of transferable features, and deal with a seires of complex and volatile scenarios in traffic state estimation.

Key Capabilities:

• Transfer learning
• Domain adaptation
• Self-adaptive model