A model predictive perimeter control with real-time partitions

Shang Jiang, Mehdi Keyvan-Ekbatani, Dong Ngoduy

Research output: Contribution to journalConference articleOther

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)292-297
Number of pages6
Issue number2
Publication statusPublished - 2021
EventIFAC Symposium on Control in Transportation Systems 2021 - Lille, France
Duration: 8 Jun 202110 Jun 2021
Conference number: 16th


  • Macroscopic fundamental diagram
  • Model predictive control
  • Network fundamental diagram
  • Network partitioning
  • Perimeter control

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