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Urban traffic control (UTC) is inherently multiscale in both temporal and spatial domains. With the wide deployment of connected and automated vehicles (CAVs), data is increasingly available to help reveal this multiscale nature of UTC and the inter-dynamics among different scales. This paper applies the multiscale UTC framework we proposed earlier and extends it to UTC on a network of traffic signals with a mixed flow of CAVs and human-driven vehicles (HDVs). We adopt distributed control as the basic scheme for network-wide control and use information sharing to achieve cooperation among different intersections. We use CAV information to estimate HDV state and develop a "safety check" technique to control CAVs in the mixed traffic flow. Together, we propose a network-wide UTC framework with mixed traffic flow. To address the computation issue of the model-based multiscale method, we develop an imitation learning (IL) enhanced data-driven method to improve the computation efficiency. Specifically, we use a convolutional neural network (CNN) to represent the policies of the slower-scale signal control problem and use the data aggregation method as the learning framework to improve the policies. This algorithm's unique feature is that IL policies' training is based on the optimized results from the model-based multiscale control method. We test the model-based and IL-based methods in simulation, under various traffic scenarios and on multiple networks. We also test the transferability property of the IL-based method by training individual intersection control separately in small networks and applying them to larger networks with various types of intersections. [ABSTRACT FROM AUTHOR]

Highlights • Provide a systematic review of CAV based UTC studies in the last few decades. • Highlight the change of design philosophy for UTC. • Summarize the benefits and drawbacks of current CAV-based UTC methods. • Predict the future study directions. Abstract Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field. [ABSTRACT FROM AUTHOR]

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. • Urban traffic control (UTC) is multi-scale in both temporal and spatial domains. • A general multi-scale control framework is developed for UTC with CAVs. • The proposed framework is applied to two-scale signal-vehicle coupled control (SVCC). • A model predictive control (MPC) scheme is developed to solve the SVCC model. • Stability analysis is conducted based on the consistency of key states of the two scales. [ABSTRACT FROM AUTHOR]