Traffic state estimation on a two-dimensional network by a state-space model

This study proposes a state-space model that estimates traffic states over a two-dimensional network with alternative routes available by a data assimilation technique that fuses probe vehicle data with a traffic flow model. Although a number of studies propose traffic monitoring methods based on physical flow dynamics using sensing data such as probe vehicle and traffic detector data, they are basically limited to traffic monitoring along a simple road section. This study extends the analysis to a two-dimensional network, in which several alternative routes exist for each OD, with consideration of the route choice behaviours of users. Our proposed method employs sequential Bayesian filtering with a cell transmission model (CTM) for the flow model and probe vehicle data. From the probe vehicle data, not only cell densities but also diverging ratios are assumed to be measured and these measurements are assimilated into the flow model. The model validation in a hypothetical network reveals the potential of the model and discloses future issues.