ABSTRACT
The exponential growth of personal vehicles, combined with an increase in trips and trip lengths, has resulted in acute road traffic congestion in most metropolitan cities around the world. A few reasons for congestion are higher demand rates, inadequate infrastructure, accidents, and construction. Data fusion is a broad area of research in which data from several sensors are combined to provide comprehensive and accurate information. Different terminologies are used related to data fusion such as information fusion, sensor fusion, multisensor data fusion, and multisensor data integration. Traffic stream models provide relationships among the three basic traffic variables—speed, flow and density—for steady-state conditions. The Kalman filter is a popular tool for recursive estimation of variables that characterize a system. The Kalman filter is used when the governing equations of the system are linear. When the system model is nonlinear, the extended Kalman filter (EKF) is typically used.
