An Information Fusion Framework of Traffic Counts Forecasts Based on Concepts from Fuzzy Set Theory.

Reliable surveillance of urban networks coupled with techniques for information acquisition on traffic states provides the basis for the deployment of Advanced Transportation Management and Information Systems (ATMIS). Since information can be collected from various sources, a gamut of approaches for the fusion of available data has been utilized in traffic control centers. This paper focuses on a special paradigm of data fusion that combines information on forecasted traffic volume obtained from a variety of alternative approaches and provides a novel forecasting scheme that treats uncertainty by adopting concepts from fuzzy set theory and expert knowledge.