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.

中文翻译：

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高速公路宏观微观交通流数据驱动优化控制策略

为了估算排放量，大多数最先进的微观排放模型（例如 VT-micro）将单个车辆的速度和加速度作为模型输入，可以通过 V2I 技术有效地收集这些数据。然而，高速公路交通控制存在空白，大多依赖宏观交通模型，忽略个体车辆状态。为了填补这一空白，本研究提出了一种利用卷积神经网络（CNN）进行高速公路主动控制的个体车辆状态预测方法。然后对路网在移动性、安全性和排放等多目标下的整体性能进行评估，以确定最优控制信号。与支持向量机和人工神经网络相比，所提出的 CNN 启用个体车辆状态预测方法报告了与地面实况数据的良好匹配。此外，还建立了基于现场数据的仿真平台，以利用 CNN 预测网络实现所提出的控制算法。结果表明，与未控制情况相比，多目标性能显着提高，与原始模型相比，实现了多目标的进一步优化。