A Dual Channel Cyber–Physical Transportation Network for Detecting Traffic Incidents and Driver Emotion
Bibliographic record
Abstract
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%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".