Data Assimilation Using a Mesoscopic Lighthill–Whitham–Richards Model and Loop Detector Data: Methodology and Large-Scale Network Application

Abstract

Traffic managers and operators need decision support systems able to provide online traffic flow monitoring and short-term traffic predictions on large-scale networks. Data assimilation (DA) techniques are used to combine observed data and a traffic model. This paper proposes a comprehensive data assimilation framework, based on a mesoscopic Lighthill–Whitham–Richards model, that has short computational times, is well suited for network discontinuities, provides individual vehicle tracking, and can easily be coupled with any dynamic traffic assignment model. The framework also relies on state variables that require adjustments of the DA framework. The requirements proposed by the paper are concerned with (a) the model numerical scheme, (b) the traffic state transformation operators, and (c) updating of the model. The proposed DA framework is first applied to a simplified network. It validates the ability of the proposed framework to update and propagate traffic states accordingly. The proposed framework is then applied to a real large-scale network. The results demonstrate its ability to monitor and forecast traffic conditions with online capabilities.