Traffic Accident Prediction and Warning System: Integration Use Case
Ghaffari, Amirhossein; Nguyen, Huong; Saleh, Alaa; Lovén, Lauri; Gilman, Ekaterina (2024-06-29)
29.06.2024
Ghaffari, A., Nguyen, H., Saleh, A., Lovén, L., & Gilman, E. (2024). Traffic accident prediction and warning system: Integration use case. Fourth Workshop on Knowledge-infused Learning (KIL 2024).
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© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202411016566
https://urn.fi/URN:NBN:fi:oulu-202411016566
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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.
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.
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