Time aware hybrid hidden markov models for traffic congestion prediction

Abstract

Traffic Congestion is a socio-economic problem that swelled in the past few decades. Intelligent Transportation Systems (ITS) has become the cutting edge solution to most traffic problems. One of the important problems is the prediction of the incoming traffic pattern. There are a number of available approaches for traffic congestion prediction. One approach using NeuroFuzzy is discussed here. The approach is modified into a hybrid one using Hidden Markov Models (HMM). HMM is implemented to take into consideration time factor. It is used to select the right NeuroFuzzy network suitable for this particular time period for efficient congestion prediction. The novelty in this research is: 1) showing that the right choice of traffic pattern for training affects the quality of the prediction dramatically. 2) The results from the hybrid model showing 6% MAE rate which outperforms the standard standalone NeuroFuzzy approach of 15% error.