Real-time data fusion of road traffic and ETC data for road network monitoring

In our present work we introduce the use of data fusion in the field of Transportation and more precisely for motorway travel time estimation. We present an Ad-hoc approach as the operational foundation for the development of a novel travel time estimation algorithm, called Modified Cumulative Traffic Counts Method (MCTC). Based on a data fusion paradigm, we combine in real time multiple evidence derived from two complementary sources to feed our MCTC inference engine and attempt to best estimate prevailing travel time. Our approach has as its main advantages the modeling power of Theory of Evidence in expressing beliefs in some hypotheses, the ability to add the notions of uncertainty in terms of confidence interval. We evaluate our travel estimation algorithm prototype through a set of experiments that were conducted with real network traffic. We conclude that data fusion is a promising approach as it increases the estimation and prediction capability of our MCTC algorithm and increase the robustness of the estimation process.