Model predictive control for freeway networks based on multi-class traffic flow and emission models


Reference:
S. Liu, H. Hellendoorn, and B. De Schutter, "Model predictive control for freeway networks based on multi-class traffic flow and emission models," IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 2, pp. 306-320, Feb. 2017.

Abstract:
The main aim of this paper is to use multi-class macroscopic traffic flow and emission models for MPC for traffic networks. Particularly, we use and compare extended versions of multi-class METANET, FASTLANE, multi-class VT-macro, and multi-class VERSIT+. Besides, end-point penalties based on these multi-class traffic flow and emission models are also included in the objective function of MPC to account for the behavior of the traffic system beyond the prediction horizon. A simulation experiment is implemented to evaluate the multi-class models. The results show that the approaches based on multi-class METANET and the extended emission models (multi-class VT-macro or multi-class VERSIT+) can improve the control performance for the total time spent and the total emissions w.r.t. the non-control case, and they are more capable of dealing with the queue length constraints than the approaches based on FASTLANE. Including end-point penalties can further improve the control performance with a small sacrifice in the computational efficiency for the approaches based on multi-class METANET, but not for the approaches based on FASTLANE.


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Bibtex entry:

@article{LiuHel:15-020,
        author={S. Liu and H. Hellendoorn and B. {D}e Schutter},
        title={Model predictive control for freeway networks based on multi-class traffic flow and emission models},
        journal={IEEE Transactions on Intelligent Transportation Systems},
        volume={18},
        number={2},
        pages={306--320},
        month=feb,
        year={2017},
        doi={10.1109/TITS.2016.2573306}
        }



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