Dynamic and heterogeneity-sensitive urban network partitioning: a data-driven technique

Abstract

Information of Connected Vehicles (CVs) could describe vehicular dynamics in much greater detail, enhancing the effectiveness of traffic control systems. One important such system is perimeter control, which can achieve better performance by incorporating the evolution of congestion into the identification of protected regions through a dynamic approach. However, little attention has been given to identifying such dynamic regions by developing CV-based network partitioning models in a spatiotemporal dimension. To address this gap, this paper proposes a three-module framework that (1) collects the relevant information of CVs, (2) performs initial partitioning based on some rational considerations, and (3) identifies the optimal protected regions through a partitioning evaluation, improvement, and iteration algorithm. The carried-out comparisons between perimeter control systems employing the resulting protected regions and those using static regions confirm that the proposed framework enhances the efficiency of perimeter control, even for CVs' penetration rates that are as low as 15%.