Real Time Traffic Monitoring With Bayesian Belief Networks
conference paper
Modern traffic management systems are, we believe, best implemented as multi-agent systems. When multiple agents have to make decisions on shared knowledge, this knowledge incorporates the uncertainty of underlying information and sensor systems. One approach to deal with uncertainty is the use of probabilistic models called Belief Networks. However, calculating with these models is a NP-hard problem. In order to apply this technology we had to break down its complexity for our specific case. This paper discusses the design choices that we made to boost the performance of our Bayesian belief network and thereby enabling this technique for real-time traffic monitoring in multi-agent systems.
TNO Identifier
222270
Source title
12th World Congress on Intelligent Transport Systems, 6-10 November 2005, San Francisco, CA, USA
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