# Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control
## 1 Introduction  
As people's demand for driving increases, freeways have rapidly reached saturation and traffic congestions occur frequently. In China, reoccurred congestion takes up a large proportion, contributing to wasting of people's time, as well as economic loss. In addition, due to serious environmental pollution and resources shortages, it is important to pay attention to emissions and fuel wastage resulting from traffic congestion. Therefore, it is important to handle congestion through reasonable freeway control methods, considering the limited resources. This paper proposes a more scientific and effective control method to alleviate congestion, reduce pollution and energy wastage. This paper mainly focuses on the freeway control in a real network with an on-ramp. It uses METANET model to simulate traffic behaviors of the freeway, and VT-micro for estimating emissions and fuel consumption. The authors of [1] provide a general framework to integrate these two kinds of models, the macroscopic traffic flow model METANET and the microscopic emission and fuel consumption model VT-micro, resulting in the so called the \"VT-macro\" model. Most of the papers consider the variable speed limit (VSL) as continuous values, however, in this paper, the VSL values are treated as discrete values following the ideas in [2], which, not only shows a good performance, but also keeps the computation time reduced. In this article, a model predictive control method based on dynamic multi-objective optimization algorithm, named as MPC_CPDMO-NSGA-II, is designed and tested under the simulation environment. Furthermore, in order to provide more reasonable and effective freeway control solutions, a new dynamic multi-objective optimization algorithm based on clustering and prediction strategy, named as CPDMO-NSGA-II, is proposed to realize on-line optimization in model predictive control at each control step. The aim of the paper is to relieve freeway congestion, reduce fuel consumption and emission simultaneously based on a model predictive control method. Considering that the essence of the freeway control problem mentioned above is a multi-objective control problem, four performance indicators are considered in this paper, which is to minimize the total time spent, total distance, fuel consumption and emissions simultaneously. The classical way of dealing with multiple conflict objectives in the model predictive control method is to sum up the different objectives into a single comprehensive performance indication by a predefined threshold. Its aim is to translate the multi-objective problem into a single objective control problem. Although the weight sum method is easy to realize, the threshold must be predefined according to expert experience and better trade-off among different conflict objectives cannot be guaranteed, especially when the search space is non-linear, non-convex, discontinuous, etc. Therefore, it is meaningful to use a multi-objective optimization algorithm to optimize the multiple conflict objectives simultaneously in the model predictive control method. At the same time, since the model predictive control method has the characteristics of model prediction, receding horizon and feedback correction, it is not enough to replace the weight sum method with the multi-objective optimization algorithm and solve it using the classical static multi-objective optimization algorithm. Therefore, a new dynamic multi-objective genetic algorithm, CPDMO-NSGA-II, is proposed in this paper to realize on-line multi-objective optimization in the model predictive control. There are two purposes for replacing the weight sum method with the dynamic multi-objective optimization algorithm, first is to enable the algorithm to adapt to the environmental variation and converge to the optimal solution at each control step as soon as possible and second is to realize better trade-off among different multiple objectives.