Transportation Systems (ITS) applications, where every new traffic data become available in
every few minutes or seconds, the main objective of this study is to perform a multi-step
ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational
load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory,
two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction …