The application of space-time ARIMA model on traffic flow forecasting

Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Space-time autoregressive time series modeling is a promising inductive method that uses a small number of parameters and can be used for online monitoring and prediction. In this paper, we develop space-time autoregressive models for urban traffic flow network scenarios. We evaluate the ability of the space-time autoregressive models to model the spatial and temporal correlations in the traffic network and show that the space-time model performs well.