A multi-scale control framework for urban traffic control with connected and automated vehicles

Qiangqiang Guo and Ban, Xuegang (Jeff)

Transportation Research Part B: Methodological, 2023, vol. 175, issue C

Abstract: Urban traffic control is multi-scale in both temporal and spatial domains, which is of great importance to improve the quality and efficiency of our transportation system. In this paper, we propose a general multi-scale modeling and control framework for urban traffic control with connected and automated vehicles (CAVs). We apply the proposed multi-scale framework to explicitly model and solve the two-scale signal-vehicle coupled control (SVCC) problem under the environment of full penetration of CAVs. To solve the SVCC problem, we propose a model predictive control (MPC) scheme and develop a stability analysis method based on the concept of consistency of key states between the two scales. We show numerical results of the SVCC model and comparisons with some benchmark methods. We also discuss how to extend the SVCC model to the mixed traffic flow and tri-scale/multi-scale urban traffic control problems.

Keywords: Connected and automated vehicles; Multi-scale modeling and control; Signal-vehicle coupled control; Model predictive control; Stability analysis (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.trb.2023.102787

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