A model predictive perimeter control with real-time partitions. Issue 2 (2021)

• Record Type: Journal Article
• Authors: Jiang, Shang; Keyvan-Ekbatani, Mehdi; Ngoduy, Dong
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 2(2021)
• Page Start: 292
• Page End: 297
• Publication Date: 2021
• Subjects: Perimeter control -- Macroscopic fundamental diagram -- Network fundamental diagram -- Network partitioning -- Model predictive control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.06.052
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Previous studies through simulation and empirical data have shown that a Network Macroscopic Fundamental Diagram (NMFD) exists and can be used for designing network optimal perimeter control strategies. These control strategies rely on well defined NMFDs, which highly depend on the homogeneity of the traffic condition in the network. However, it is known that traffic dynamics change drastically during the day in different zones in a large-scale network, and different control strategies might lead to heterogeneous traffic distribution across the urban network. One potential direction is re-partitioning the network to maintain the well defined NMFDs. However, re-partitioning the network changes each sub network's size, such that it makes the well-defined NMFDs unpredictable. This paper provides a model predictive control-based optimization approach for perimeter control using real-time partitioning to avoid this problem and utilize re-partitioning techniques. Results show that the proposed method can be used in a heterogeneous network to improve control performance by redistributing accumulations via re-partitioning over time. Our results, which are compared to no control and the traditional model predictive control, yield that the proposed method is superior to the others.