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.