A New Combinatorial Characteristic Parameter for Clustering-Based Traffic Network Partitioning

Traffic network partitioning is of great importance in regional coordinated traffic signal control in urban areas. Most partitioning algorithms only use a single traffic parameter to represent dynamic traffic information, which will lead to inaccurate results. Moreover, traditional clustering and heuristic partitioning algorithms are not practical in applications. Thus, in this paper, we first propose a new combinatorial characteristic parameter for clustering-based partitioning algorithm by using the Pearson correlation coefficient and data normalization. Then, we refer to the idea of “snake” algorithm and use a linear programming model to obtain the exact partitioning result, and such algorithm avoids local optimum of heuristic algorithms. Finally, based on the real traffic data of a Chinese city, we conduct the experiments and verify the effectiveness of the new combinatorial parameter.