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Real Time Traffic Monitoring With Bayesian Belief Networks

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.