Short-term traffic flow forecasting with A-SVARMA

Abstract

Short-term Traffic Flow Forecasting (STFF), the process of predicting future traffic conditions based on historical and real-time observations, is an essential aspect of Intelligent Transportation Systems (ITS). The existing well-known algorithms used for STFF include time-series analysis based techniques, among which the seasonal Autoregressive Moving Average (ARMA) model is one of the most precise methods used in this field. The effectiveness of STFF in an urban transport network can be fully be realized only in its multivariate form where traffic flow is predicted at multiple sites simultaneously. In this paper, this concept in explored utilizing an Additive Seasonal Vector ARMA (A-SVARMA) model to predict traffic flow in short-term future considering the spatial dependency among multiple sites. The Dynamic Linear Model (DLM) representation of the A-SVARMA model has been used here to reduce the number of latent variables. The parameters of the model have been estimated in a Bayesian inference framework employing a Markov Chain Monte Carlo (MCMC) sampling method. The efficiency of the proposed prediction algorithm has been evaluated by modelling real-time traffic flow observations available from a certain junction in the city centre of Dublin.