Short-Term Traffic State Prediction Based on Temporal–Spatial Correlation

Abstract

The stochastic cell transmission model (SCTM) was originally developed for stochastic dynamic traffic state modeling under several assumptions, e.g., the independent/uncorrelated assumption of the underlying stochastic processes governing demand and supply uncertainties. However, traffic flow, by nature, is correlated in both spatial and temporal domains due to its dynamics, similar environmental conditions and human behaviors. The independent assumption in the original SCTM framework may prevent the model from a broad range of applications, e.g., short-term traffic state prediction. In this paper, the SCTM framework is extended to consider the spatial-temporal correlation of traffic flow and to support short-term traffic state prediction. First, a multivariate normal distribution (MND)-based best linear predictor is adopted as an auxiliary dynamical system to the original SCTM to forecast boundary variables and/or supply functions. The predicted boundary variables and supply functions are taken as inputs to the SCTM to perform short-term traffic state prediction. The independent assumption of the SCTM is relaxed by incorporating the covariance structure calibrated from the spatial correlation analysis for probabilistic traffic state evaluation. For real-time application purposes, prediction is conducted in a rolling horizon manner, which is useful for adjusting the predicted traffic state using real-time measurements. The proposed traffic state prediction framework is validated by empirical studies that demonstrate the effectiveness of the proposed method.