Correlation degree analysis of arterial adjacent intersections for coordinated control subunit partition

Abstract

To analyze the correlation between adjacent intersections and implement coordinated signal control on arterial roads is a burning issue. The vehicle detector is indispensable by which the traffic state data are collected directly to get a reasonable control strategy. The traditional methods have poor control performance because of lacking enough accuracy. In this article, the new layout method of vehicle detector is proposed to collect and store high-resolution traffic data continuously, which can identify a critical turning point of the change in traffic state. Thus, a correlation degree model to quantitative relevance between adjacent intersections on arterial road based on traffic status data is established. This model could make more exact measurement in order to achieve the control subunit partition of arterial roads, selecting density-based spatial clustering of applications with noise algorithm to cluster the deriving correlation indexes. The partition method is evaluated by an arterial road including 12 intersections, and this road is divided into five subunits. The simulation result validates that the partition method based on correlation indexes can significantly improve the operation efficiency of arterial roads and reduce the traffic delay. This research may provide a new strategy on the partition of control unit for arterial road more accurately based on high-resolution traffic data.