Urban transportation emissions mitigation: Coupling high-resolution vehicular emissions and traffic models for traffic signal optimization

This paper proposes a methodology that allows high‐resolution traffic and emissions models, known as microscopic simulation models, to be efficiently used to address transportation optimization problems that account for complex environmental metrics. The methodology consists of a metamodel simulation‐based optimization (SO) approach. The metamodel combines traffic and emissions information from high‐resolution microscopic simulators with information from lower‐resolution analytical macroscopic models. This paper formulates and uses an analytical and differentiable macroscopic approximation of the non‐differentiable simulation‐based microscopic emissions model. A differentiable macroscopic traffic model is also used. This paper shows that the analytical structural information provided by macroscopic analytical emissions models can contribute, despite their lower‐resolution, to enhance the computational efficiency of algorithms that embed higher‐resolution inefficient emissions models. The proposed algorithm is computationally efficient, i.e., it can identify points with improved performance within few simulation runs. More generally, the results of this paper highlight the added value of embedding analytical structural information within SO algorithms to address complex SO problems. A traffic signal control case study is carried out. The proposed metamodel identifies signal plans that improve travel time and various emissions metrics. We present the corresponding monetary savings that can be achieved. This optimization framework enables practitioners to use high‐resolution microscopic traffic and emissions models to systematically design transportation schemes that account directly, in the design process, for environmental metrics. Hence, the use of such high‐resolution models is no longer limited to the environmental evaluation of a small set of predetermined schemes. The tight computational budgets used in this paper show that such complex problems can be addressed in a computationally efficient manner.