期刊名称：International Journal on Electrical Engineering and Informatics
印刷版ISSN：2085-6830
出版年度：2019
卷号：11
期号：1
页码：1-17
DOI：10.15676/ijeei.2019.11.1.1
出版社：School of Electrical Engineering and Informatics
摘要：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 trafficproblems. One of the important problems is the prediction of the incoming traffic pattern. Thereare a number of available approaches for traffic congestion prediction. One approach usingNeuroFuzzy is discussed here. The approach is modified into a hybrid one using Hidden MarkovModels (HMM). HMM is implemented to take into consideration time factor. It is used to selectthe right NeuroFuzzy network suitable for this particular time period for efficient congestionprediction. The novelty in this research is: 1) showing that the right choice of traffic pattern fortraining affects the quality of the prediction dramatically. 2) The results from the hybrid modelshowing 6% MAE rate which outperforms the standard standalone NeuroFuzzy approach of 15%error.
关键词：Hidden Markov Models; NeuroFuzzy; Traffic Time Effect; Traffic Congestion;Prediction; and Empirical Evaluation
