According to recent studies, beyond being a major worldwide problem with huge economic impact, traffic collisions are poised to become as well one of the most important leading causes of death. Proactive traffic enforcement and intervention should be based on a thorough analysis on the collision data available to identify leading causes of accidents, the most prone locations as well as to predict the conditions for collision occurrence. This paper presents a novel framework for collision prediction that takes into consideration historical and real-time factors, such as weather, geospatial information and social events data that can be obtained with existing sensor technology. A prototype is proposed, implemented and evaluated for the city of Ottawa, Canada.

Additional Metadata
Keywords Classification, Machine learning, Prediction, Sensor data, Traffic collisions
Persistent URL dx.doi.org/10.1109/ROSE.2019.8790431
Conference 13th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019
Citation
Reveron, E. (Enrique), & Cretu, A.M. (2019). A framework for collision prediction using historical accident information and real-time sensor data: A case study for the city of ottawa. In IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings. doi:10.1109/ROSE.2019.8790431