HERMES model advances traffic conflict prediction using graph neural networks

Researchers have developed HERMES, a novel graph neural network designed for predicting traffic conflicts at signalized intersections. This model represents vehicles and pedestrians as heterogeneous nodes and their interactions as relation-specific edges, incorporating kinematic and safety descriptors. HERMES demonstrated superior performance compared to Transformer and XGBoost baselines in detecting conflict sequences, achieving an AUC-ROC of 0.9898 and an AUC-PR of 0.9412 in evaluations. AI

IMPACT This research could lead to more proactive safety measures in traffic management systems.

RANK_REASON The cluster contains a research paper detailing a new model and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• HERMES
• Hugging Face
• Md Monzurul Islam
• ScienceCast
• State Space Model
• Transformer++
• XGBoost

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