Generalized Regression Neural Network for predicting traffic flow

Abstract

Forecasting traffic flow is a popular research topic in Intelligent Transportation System. There have been several methods used for this forecasting, such as statistical methods, Bayesian Network, Neural Network Model, Hybrid ARIMA and ANN. Generalized Regression Neural Network (GRNN) is an interesting model to be used in forecasting traffic flow, as it can predict data with dynamic change and non-linear in nature, which is generally found in traffic flow data. In this research, a GRNN model is set up to process traffic flow data, and comparing its results and the results from other predicting methods (ARIMA, Single Exponential Smoothing, and Moving Average). Leave One Out Cross Validation (LOOCV) is used in testing traffic flow data and Mean Absolute Percentage Error (MAPE) is used as the evaluation criterion in the testing. The results show that using GRNN method on the testing data can improve the accuracy of predictions by reducing the value of MAPE when three other predicting methods: ARIMA, Single Exponential Smoothing, and Moving Average.