Study of a Spatial Structure of Urban Traffic Flows Using a Regime-Switching Vector Autoregressive Model

Dmitry Pavlyuk

Proceedings of the Second Workshop on Computer Modelling in Decision Making (CMDM) (2017)
Keywords: spatiotemporal modelling, traffic flow, vector autoregressive model, regime-switching model, forecasting

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This paper is devoted to multivariate regime-switching vector autoregressive models application taking into account a spatial structure in urban traffic flows. The spatial structure of traffic flows is estimated as statistical relationships between traffic characteristics (including lagged) at different road segments. A broad literature review is provided and complex spatial dependencies at road segments are illustrated.
We execute empirical analyses of out-of-sample forecasting accuracy of several widely used traffic flow models: autoregressive integrated moving average model, vector autoregressive model, and Markov-switching vector autoregressive model. We provided empirical evidence for improving forecasting accuracy by including regime-dependent spatial dependencies of traffic flows at road segments into vector autoregressive models. Special attention is paid to selection of time resolution and its effects on the forecasting results.