Network Fundamental Diagram based Dynamic Routing in a Clustered Network

Dynamic routing algorithms aim to find the shortest (fastest in most cases) path in a road network prone to timedependent traffic states. Conventional approaches assume the availability of link-level travel time data. Due to the limited number of sensors in real road networks, for large parts of a road network often no travel time data are available. Linklevel travel times are therefore often estimated as constants. Consequently, predicted travel times and routes are not accurate, especially under congested traffic conditions. In this paper, we develop a macroscopic routing algorithm in a clustered network based on loop detector data. Traffic speeds in each cluster are assumed to scale homogeneously and are estimated based on the cluster-specific network fundamental diagrams. A macroscopic routing approach is implemented, which reduces the complexity of finding an optimal path. As a result, missing link-level data are imputed with an expected traffic state in each cluster based on the fundamental diagram. Preprocessed routing information within the clusters and a macroscopic network lead to fast route computations. The approach is evaluated from two sides. Using one month of processed empirical trajectory data collected from a large fleet of vehicles in Munich, our predicted travel times are proved to be more accurate compared to a baseline routing algorithm and a one-cluster (network) method. Re-routing can also be observed from free-flow routes using synthesized trips, showing that our macroscopic routing algorithm is capable of avoiding congested clusters.