Real-time Traffic Congestion Prediction: A Novel Online Learning Method with Multi-Head Attention Mechanism and LSTM-Based Integrated Learning Harbin Institute of Technology, School of Transportation Sci

Format:

Book

Conference/Event

Author/Creator:

Fu, Chuanyun, author.

Contributor:

Bai, Wei

Liu, Huahua

Liu, Jiaming

Lu, Zhaoyou

Wumaierjiang, Ayinigeer

Conference Name:

2025 International Conference on Intelligent Transportation and Future Mobility (ITFM2025) (2025-04-11 : Guilin, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Real-time traffic congestion prediction is essential for proactive traffic management, as it enhances the responsiveness of traffic systems, including route guidance, control, and enforcement. However, the heavy reliance on extensive historical data presents a significant challenge for real-time model updates. To overcome this limitation, this study proposes an advanced online learning framework that integrates a multi-head attention mechanism with LSTM-based ensemble learning. This approach incorporates traffic congestion factors as input features and employs average delay per kilometer as the predictive output. The experimental findings indicate that: 1) the proposed approach successfully enables real-time traffic congestion forecasting, and 2) it demonstrates strong adaptability in dynamic traffic environments

Notes:

Vendor supplied data

Publisher Number:

2025-99-0420

Access Restriction:

Restricted for use by site license