Subarea Partition Based on Correlation Analysis with Edge-Elimination Strategy Using Automatic License Plate Recognition Data

Abstract

To partition an urban network into several subareas (i.e., subarea partition) is a vital step for regional coordinated signal control. The correlation between intersections must be analyzed for achieving reliable subarea partition results. However, because of the incompleteness of spatial–temporal information in traffic data, previous studies merely explored the relationship between any intersections. Subarea partition considering the correlation of any pair of intersections remains a challenge in a large-scale network. This paper proposes a subarea partition method that integrates a novel correlation-degree model and the Newman fast algorithm with an edge-elimination strategy using automatic license plate recognition (ALPR) data. First, vehicle trips are extracted and a correlation-degree model is developed for measuring the relationship of any pair of intersections. Second, an edge-elimination strategy is proposed to generate candidate subarea partition solutions under conditions with different proportions of correlated edges. Finally, an optimal solution of subarea partition is identified by the ratios of ideal connected intersections to total intersections of different partition solutions’ correlation index (CI). The proposed method was implemented in a real-world urban network in Kunshan, China. The results show that the optimal partition solution can be obtained when the top 33% of correlated edges are maintained, and the ratio of ideal connected intersections’ CI is 72.35% with most of the intersections being connected, which demonstrates the rationality of the proposed partition method in large-scale urban networks.