Short-Term Prediction of Traffic Flow Status for Online Driver Information

摘要

The principal aim of this study was to develop a method for making a short-term prediction model of traffic flow status (i.e. travel time and a five-step travel-speed-based classification) and test its performance in the real world environment. Specifically, the objective was to find a method that can predict the traffic flow status on a satisfactory level, can be implemented without long delays and is practical for real-time use also in the long term. A sequence of studies shows the development process from offline models with perfect data to online models with field data. Models were based on MLP neural networks and self-organizing maps. The purpose of the online model was to produce real-time information of the traffic flow status that can be given to drivers. The models were tested in practice.