An identification model of critical control sub-regions based on macroscopic fundamental diagram theory

Abstract

As traffic congestion intensifies, identification and control of critical areas to balance road network traffic flow are of paramount importance to mitigate and prevent traffic congestion, based on the road network traffic realities. Most previous traffic sub-region division methods of static characteristics are not suitable for dynamic traffic management. This article proposes the division model of traffic sub-regions and identification method of critical control areas based on macroscopic fundamental diagram (MFD) theory closely integrated with dynamic characteristics of road network traffic. To be specific, the existence of small-scale road network MFD is confirmed at first through the traffic data transmitted from Jiangbei district in Chongqing, China. The initial control sub-regions of the road network are formulated with each small-scale road network as a control area. Then, the traffic state value of sub-regions is calculated by means of regression analysis. A set of rules are designed to aggregate the initial sub-regions, which are associated with the traffic state and geographically adjacent, and the adjusted traffic sub-regions are obtained by means of correlation coefficient analysis. Finally, the identification method of traffic critical control sub-regions is proposed based on clustering analysis. In addition, the identification model of critical control sub-regions is applied and analyzed in the experimental road network of Jiangbei district in Chongqing, China. The results demonstrate that the proposed model is flexible and efficient enough to improve the control over road networks, promoting application of traffic sub-region division based on MFD in dynamic traffic control over actual road networks.