Spatiotemporal Forecasting of Urban Traffic Flow Volatility

Abstract

Spatiotemporal relationships are widely used in urban traffic modelling for obtaining accurate and explainable forecasts. However, the majority of studies are focused on point forecasts and do not pay attention to a time-dependent variability of traffic flows and related volatility of model errors. At the same time, traffic volatility is an important value that can be effectively used for the construction of confidence intervals for traffic forecasts and detection of traffic anomalies. This study proposes an application of the spatiotemporal approach to modelling of traffic volatility and predicting the conditional variance of traffic forecasts. The approach is based on a spatiotemporal specification of the popular generalized autoregressive conditional heteroskedasticity (GARCH) model that is combined with the spatially regularised vector autoregressive model as a multivariate urban traffic predictor. The resulting hybrid model was applied for a large real-world data set and demonstrated its performance for traffic volatility prediction. The proposed approach was compared to existing univariate traffic volatility models and proved its utility for deeper understanding of traffic variance and anomalies.