# Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization
## 6 Conclusions  
In this paper, the robust dynamic traffic multi-objective optimization problem of a multi-class expressway is studied. By considering three traffic indicators--travel time, ramp queue and traffic emissions--at the same time, the implementation of variable speed limits and ramp metering strategies on an urban expressway is explored in this paper. Based on the multi-class macroscopic traffic flow model multi-class METANET and the multi-class emission and fuel consumption model multi-class VT-macro, a multi-on-ramp and multi-off-ramp expressway model is proposed. The multi-on-ramp and multi-off-ramp expressway system optimization problem is described as a robust dynamic multi-objective optimization problem. The DFCM-RDNSGA-III algorithm is proposed to solve the variable speed limit and ramp metering scheme under this problem, and the algorithm effect is verified based on VISSIM simulation. In general, compared with NSGA-III and RDNSGA-III, DFCM-RDNSGA-III, a robust dynamic genetic algorithm based on density fuzzy c-means clustering method, can better deal with the robust dynamic optimization problem which considers environmental factors and traffic congestion. In future research, the robust dynamic multi-objective optimization algorithm needs to be improved in the following aspects: the high evolution pressure when the dimension is high, the insufficient factors considered in the evolution process to adapt to the complexity of high-dimensional problems, the irregular Pareto front exploration, and the balance between dynamic capabilities and robustness. Moreover, the existing traffic flow model does not match the actual traffic operation completely. Factors affecting the traffic condition must be studied from more aspects, and a traffic flow model better matching the actual road network must be designed. Funding acquisition, J.C.; methodology, J.C.; project administration, J.C.; software, Q.F. and Q.G. ; supervision, J.C.; validation, Q.G. ; visualization, Q.F. and Q.G. ; writing--original draft, J.C.; writing--review & editing, Q.F. and Q.G. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number 61104166. We declare there are no conflict of interest regarding the publication of this paper. We have no financial and personal relationships with other people or organizations that could inappropriately influence our work. Figure 9: Truck flow in each road section of three algorithms. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON], [PERSON] Model Predictive Control for Freeway Networks Based on Multi-Class Traffic Flow and Emission Models. _IEEE Trans. Intell. Transp. Syst._**2016**, _18_, 306-320. [CrossRef]
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