A macro-microscopic traffic flow data-driven optimal control strategy for freeway

To estimate the amount of emissions, most state-of-the-art microscopic emission models, such as VT-micro, takes the individual vehicle speed and acceleration as the model input, which can be collected efficiently with V2I technology. However, there is a gap in freeway traffic control since most of them rely on the macroscopic traffic model and omit the individual vehicle status. To fill this gap, this study proposed an individual vehicle status prediction method that utilized the convolutional neural network (CNN) for freeway proactive controls. Then the overall performance of the road network in multi-objective, namely mobility, safety, and emissions, will be evaluated to determine the optimal control signal. The proposed CNN enabled individual vehicle status prediction method reported a good match to the ground truth data compared with the support vector machine and artificial neural network. Furthermore, a field data-based simulation platform was established to implement the proposed control algorithm with the CNN prediction network. The result showed that the multi-objective performance was significantly improved compared with the uncontrolled case and achieved further optimization of multi-objective compared with the original model.