 Enhancing model-based feedback perimeter control with data-driven online adaptive optimizationAnastasios Kouvelas, Mohammadreza Saeedmanesh and Nikolas Geroliminis Transportation Research Part B: Methodological, 2017, vol. 96, issue C, 26-45 Abstract: Most feedback perimeter control approaches that are based on the Macroscopic Fundamental Diagram (MFD) and are tested in detailed network structures restrict inflow from the external boundary of the network. Although such a measure is beneficial for the network performance, it creates virtual queues that do not interact with the rest of the traffic and assumes small unrestricted flow (i.e. almost zero disturbance). In reality, these queues can have a negative impact to traffic conditions upstream of the protected network that is not modelled. In this work an adaptive optimization scheme for perimeter control of heterogeneous transportation networks is developed and the aforementioned boundary control limitation is dropped. A nonlinear model is introduced that describes the evolution of the multi-region system over time, assuming the existence of well-defined MFDs. Multiple linear approximations of the model (for different set-points) are used for designing optimal multivariable integral feedback regulators. Since the resulting regulators are derived from approximations of the nonlinear dynamics, they are further enhanced in real-time with online learning/adaptive optimization, according to performance measurements. An iterative data-driven technique is integrated with the model-based design and its objective is to optimize the gain matrices and set-points of the multivariable perimeter controller based on real-time observations. The efficiency of the derived multi-boundary control scheme is tested in microsimulation for a large urban network with more than 1500 roads that is partitioned in multiple regions. The proposed control scheme is demonstrated to achieve a better distribution of congestion (by creating “artificial” inter-regional queues), thus preventing the network degradation and improving total delay and outflow. Keywords: Real-time urban perimeter control; Macroscopic fundamental diagram; Linear feedback regulators; Online learning; Adaptive optimization; Adaptive fine-tuning (AFT) (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:transb:v:96:y:2017:i:c:p:26-45 Ordering information: This journal article can be ordered from DOI: 10.1016/j.trb.2016.10.011 Access Statistics for this article Transportation Research Part B: Methodological is currently edited by Fred Mannering More articles in Transportation Research Part B: Methodological from Elsevier |
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