A study of hybrid neural network approaches and the effects of missing data on traffic forecasting

In this pal)er we present an application of hybrid neural network approaches and an assessment of the effects of missing data on motorway traffic flow forecasting. Two 1 ybrid aj)13roaches are developed using a Self-Organising Map (SOM) to initially, classify traffic into different states. The first hybrid approach includes four Auto-Regressive Integrated Moving Average (ARIMA) models, whilst the second uses two Multi-Layer Perception (MLP) models. It was found that the SOM/ARIMA hybrid approach out-performs all individual ARIMA models, whilst the SOM/MLP hybrid approach achieves superior forecasting performance to all models used in this study, including three naive models. The effects of different proportions of missing data on Neural Network (NN) performance when forecasting traffic flow are assessed and several initial substitution options to replace missing data are discussed. Overall, it is shown that ARIMA models are more sensitive to the percentage of missing data than neural networks in this context.