Traffic Accident Prediction and Warning System: Integration Use Case

Amirhossein Ghaffari, Huong Nguyen, Alaa Saleh, Lauri Lovén, Ekaterina Gilman

Keywords: Smart City, Transportation, Federated Learning, Edge Computing, Generative AI, RAG

Abstract: This paper presents a system for predicting and warning about traffic accidents in smart cities, aimed at enhancing urban safety through advanced data analysis and explained warning and reporting. Our system emphasizes computational efficiency and data privacy, predicting traffic accident severity with good accuracy. By integrating real data with external knowledge sources, the system produces detailed, contextually relevant reports and warnings. Implemented with effective task orchestration, our system ensures seamless integration and resource management. Evaluation results demonstrate high accuracy and scalability, highlighting its potential for practical application in smart city environments. Future work will focus on further enhancing model efficiency, exploring transfer learning for broader applicability, and conducting real-world deployments to validate system performance.

Submission Number: 15