Self-adaptive event recognition for intelligent transport management
BigData Conference(2013)
摘要
Intelligent transport management involves the use of voluminous amounts of uncertain sensor data to identify and effectively manage issues of congestion and quality of service. In particular, urban traffic has been in the eye of the storm for many years now and gathers increasing interest as cities become bigger, crowded, and “smart”. In this work we tackle the issue of uncertainty in transportation systems stream reporting. The variety of existing data sources opens new opportunities for testing the validity of sensor reports and self-adapting the recognition of complex events as a result. We report on the use of a logic-based event reasoning tool to identify regions of uncertainty within a stream and demonstrate our method with a real-world use-case from the city of Dublin. Our empirical analysis shows the feasibility of the approach when dealing with voluminous and highly uncertain streams.
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关键词
transportation systems stream reporting,event processing,pattern recognition,inference mechanisms,quality of service,traffic engineering computing,dublin,empirical analysis,pattern matching,congestion management,event calculus,intelligent transport management,urban traffic,uncertain sensor data,self-adaptive event recognition,logic-based event reasoning tool,intelligent transportation systems,sensor fusion
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