A Mixture Model-based Clustering Method for Fundamental Diagram Calibration Applied in Large Network Simulation

Abstract

In traditional methods, fundamental diagrams (FDs) were calibrated offline with a limited number of links. Although few recent studies have paid attention to employing cluster techniques to calibrate link FDs for network level analysis, they were mainly focused on heuristic clustering methods, such as k-means and hierarchical clustering algorithm which might lead to poor performance when there are overlaps between clusters. This paper proposed a mixture model-based clustering framework to calibrate link FDs for network level simulation. This method can be applied to discover a relatively small number of representative link FDs when simulating very large networks with time and budget constraints. In addition, the proposed method can be used to investigate the spatial distribution of links with similar FDs. The proposed method is tested with 567 links using one year’s data from the Northern California.