Macroscopic traffic model for large scale urban traffic network design

Abstract

This paper presents a flexible macroscopic traffic model that is applicable for large scale urban traffic network simulations. The proposed approach combines several advantages. First, the discretized partial differential equation of the macroscopic model is solved numerically with low computational effort. The spatial and temporal resolution of the discretization are tuning parameters to balance computational effort with model accuracy. Second, the model allows arbitrary functional forms of the fundamental diagram defined by a small number of parameters. Thereby, moving density gradients (jam fronts) are represented accurately. The model parameters are physically meaningful and can readily be estimated from measurement data. Third, two general types of intersection handling are proposed and can be combined with different merge models. The first intersection approach is a binary traffic light, similar to real traffic lights. Detailed insights concerning queue length, flow across intersections and routing decisions can be investigated. The second approach is a continuous valve-like approach that allows investigation of averaged effects and large scale interaction and feedback effects. Fourth, the proposed model scales linearly with spatial and temporal resolution as well as network size and can be partially solved in parallel to increase computational efficiency. To demonstrate the above mentioned qualities of the presented model, two realistic example situations and a comprehensive study on the scaling properties are provided.