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:
In this paper we develop and investigate some multi-class macroscopic traffic flow and emission models: a new multi-class METANET model, and two new emissions models: multi-class VT-macro and multi-class VERSIT+. To allow comparison with the new multi-class METANET model, we also extend the first-order multi-class traffic flow model FASTLANE with variable speed limits and ramp metering. These new multi-class macroscopic traffic flow and emission models are used as prediction models in online model predictive control for freeway networks. Besides, end-point penalties are also included to account the future extension of the traffic systems 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 developed emission models (multi-class VT-macro or multi-class VERSIT+) can improve the performance for total time spent and 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 for the setting in our experiment. Including end-point penalties can further improve the performance for the approaches based on multi-class METANET, but not for the approaches based on FASTLANE, probably due to the less reliable estimations of end-point penalties.


Downloads:
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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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