Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

Abstract. Traffic congestion is a dynamic spatial and temporal process as such might not be possible to model with linear functions of various dependent variables. That leaves lot space for non-linear approximates, neutral networks fuzzy logic. In this paper, the focus on logic approach dealing problems measuring traffic congestion. We investigate application framework selected case study, use floating car data (FCD) collected in Augsburg, Germany. A inference system built detect degrees federal highway B17. With FCD, it obtain local speed information almost all parts network. This information, together vehicle location, time heading, can further processed transformed into valuable form trip routes, travel times, etc. Initial results are compared traditional method expressing levels road network e.g. Level Service – LOS. The model, segmented mean parameters, performed well showed promising congestions. improved by involving more input density or flow, which reflect event even realistically.