A multimodal deep fusion framework for highway traffic anomaly detection

Area of Science:

• Intelligent Transportation Systems
• Deep Learning for Traffic Analysis
• Graph Neural Networks

Background:

• Traditional traffic monitoring systems struggle with accuracy and robustness due to reliance on single data sources.
• Increasing urbanization necessitates advanced solutions for real-time traffic anomaly detection and prediction.
• Complex spatiotemporal dependencies in traffic data are challenging for existing models.

Purpose of the Study:

• To propose a novel multimodal deep fusion framework for detecting and predicting abnormal traffic events.
• To enhance the accuracy, real-time performance, and robustness of traffic anomaly detection.
• To address the limitations of single-data-source monitoring systems.

Main Methods:

• Developed a multimodal deep fusion framework integrating heterogeneous graph neural networks (HGNNs).
• Enhanced the framework with an ensemble contrastive pessimistic likelihood estimation (CPLE) algorithm for robustness.
• Integrated diverse data sources: video images, traffic flow, vehicle speed, and tunnel weather conditions.

Main Results:

• The proposed MHGNN-CPLE model achieved superior performance, with 0.980 accuracy and 0.967 F1 score in static detection tasks.
• Demonstrated high precision and stability in accurately identifying abnormal traffic events across various scenarios.
• Maintained high accuracy and strong robustness under varying noise levels in dynamic traffic scenarios.

Conclusions:

• The multimodal framework effectively integrates diverse traffic data, capturing complex spatiotemporal dependencies.
• The HGNNs and CPLE algorithm provide a reliable and accurate solution for real-time traffic anomaly detection.
• Represents a significant advancement for intelligent transportation systems, improving traffic management and safety.