Short-Term Highway Traffic State Prediction Using Structural State Space Models

This research proposes a short-term highway traffic state prediction method based on a structural state space model, with the intention to provide a robust approach for obtaining accurate forecasts of traffic state under both recurring and non-recurring conditions. True traffic state is decomposed to three components, namely, regular traffic pattern, structural deviation, and random fluctuation. Particularly, the structural deviation term reflects the change of true traffic state from regular (historical) pattern, due to unexpected capacity reduction and/or demand variations. A polynomial trend is adopted to describe the temporal evolution of structural deviations across different time intervals. We derive an analytical form of structural deviations in a single bottleneck case based on cumulative flow count diagrams. The proposed model is incorporated in a Kalman filtering-based algorithmic framework, together with an adaptive scheme for determining the variances of random errors. A set of numerical experiments was conducted on two test beds in the northern Taiwan highway network. Experimental results show that the proposed approach is particularly superior to an ordinary Kalman filtering method presented in the literature under non-recurring conditions, highlighting the advantage of combining both the polynomial trend model and historical pattern into the proposed short-term traffic state prediction approach.