Deep Learning-Driven Anomaly Detection for Intelligent Transportation Networks: A Multi-Modal Data Fusion Approach

Abstract

The proliferation of data from diverse sources such as sensors, cameras, and vehicle telemetry has ushered in an era where Intelligent Transportation Networks (ITNs) can be significantly enhanced through advanced anomaly detection mechanisms. Traditional anomaly detection techniques often struggle with the complexities and scale of modern transportation data, necessitating more sophisticated approaches. This paper explores the application of deep learning for anomaly detection in ITNs through a multi-modal data fusion approach. We investigate how Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs) can be integrated to process and analyze heterogeneous data streams, enabling the detection of anomalies such as traffic accidents, unusual congestion, and infrastructure malfunctions. By fusing data from various sources, these deep learning models can capture intricate patterns and correlations, enhancing the accuracy and timeliness of anomaly detection. The study includes a detailed analysis of model architectures, data fusion techniques, and deployment strategies, as well as a discussion on the challenges associated with implementing these technologies in real-world ITNs. The findings indicate that multi-modal data fusion using deep learning holds substantial promise for developing more resilient and adaptive transportation networks, capable of effectively managing anomalies in a complex urban environment.