A Dual Channel Cyber–Physical Transportation Network for Detecting Traffic Incidents and Driver Emotion

Intelligent traffic incident detection provides benefits such as minimizing traffic accidents and fuel consumption, reducing congestion, and enhancing transportation safety. Hence, traffic incident detection has been an active research area in customer-centric intelligent transportation systems (ITS). Given that a driver’s negative emotions (e.g., anger, nervousness) are often a main cause of traffic incidents, we argue there is a close relationship between traffic incident detection and driver emotion recognition. We propose a Dual channel Dual attention Graph Attention neTworks, termed DDGAT. Specifically, the traffic channel builds a sequential-based graph, where words are nodes and their co-occurrences are edges. In contrast, the emotion channel builds a syntactic-based graph with words as nodes and semantic dependencies as edges. The first attention mechanism automatically learns the importance of neighbors in different layers for different tasks. The second attention produces the attentive graph representation for both tasks. Experiments on two benchmarking datasets including GIIE and Twitter, show the effectiveness of the proposed model over state-of-the-art baselines in terms of micro F1 and H@1, with significant improvements of 3.5%, 3.2%, 2.0%, and 1.7%.