Two-level hierarchical model-based predictive control for large-scale urban traffic networks


Reference:
Z. Zhou, B. De Schutter, S. Lin, and Y. Xi, "Two-level hierarchical model-based predictive control for large-scale urban traffic networks," IEEE Transactions on Control Systems Technology, vol. 25, no. 2, pp. 496-508, Mar. 2017.

Abstract:
Network-wide control of large-scale urban traffic networks using a hierarchical framework can be more efficient and flexible than centralized strategies for reducing the traffic congestion in big cities, because it can adequately address some problems that occur in controlling such large systems, e.g. computational complexity, multiple control objectives, weak robustness to uncertainties, and so on. In this paper, we propose a two-level hierarchical control framework for large-scale urban traffic networks. At the upper level, based on decomposing a heterogeneous traffic network into several homogeneous subnetworks, a higher-level optimization problem using the concept of macroscopic fundamental diagram is formulated to deal with the traffic demand balance problem. At the lower level, the controller with a more detailed traffic flow model for each subnetwork determines the optimal signal timing within the given region under the guidance of the upper-level controller through communication. For the application of this architecture in real time, the model-based predictive control approach is utilized so as to obtain the best solutions for both levels. Moreover, in order to decrease the computational complexity, a distributed control scheme within each subnetwork is developed at the lower level. The proposed approach is evaluated by simulation under different scenarios on a hypothetical urban traffic network, and the performance is compared with that of other control strategies.


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Bibtex entry:

@article{ZhoDeS:16-010,
        author={Z. Zhou and B. {D}e Schutter and S. Lin and Y. Xi},
        title={Two-level hierarchical model-based predictive control for large-scale urban traffic networks},
        journal={IEEE Transactions on Control Systems Technology},
        volume={25},
        number={2},
        pages={496--508},
        month=mar,
        year={2017},
        doi={10.1109/TCST.2016.2572169}
        }



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