Abstract
In USA, 2002, approximately 3.2 million intersection-related crashes occurred, corresponding to 50 percent of all reported crashes. In Japan, more than 58 percent of all traffic crashes occur at intersections. With the advances in Intelligent Transportation Systems, such as off-the-shelf and in-vehicle sensor technology, wireless communication and ubiquitous computing research, safety of intersection environments can be improved. This research aims to investigate an integration of intelligent software agents and ubiquitous data stream mining, for a novel context-aware framework that is able to: (1) monitor an intersection to learn for patterns of collisions and factors leading to a collision; (2) learn to recognize potential hazards in intersections from information communicated by road infrastructures, approaching and passing vehicles, and external entities; (3) warn particular threatened vehicles that are approaching the intersection by communicating directly to the in-vehicle system.
The work reported in this paper has been funded in part by the Co-operative Research Centre Programme through the Australian Government’s Department of Education, Science and Training.
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Keywords
- Multiagent System
- Reactive Layer
- Intersection Safety
- Intelligent Transportation System
- Intersection Agent
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Salim, F.D., Krishnaswamy, S., Loke, S.W., Rakotonirainy, A. (2005). Context-Aware Ubiquitous Data Mining Based Agent Model for Intersection Safety. In: Enokido, T., Yan, L., Xiao, B., Kim, D., Dai, Y., Yang, L.T. (eds) Embedded and Ubiquitous Computing – EUC 2005 Workshops. EUC 2005. Lecture Notes in Computer Science, vol 3823. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596042_7
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DOI: https://doi.org/10.1007/11596042_7
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