This study presents a model to characterize changes in network traffic flows as a result of implementing connected and autonomous vehicle (CAV) technology based on traffic network and built-environment characteristics. To develop such a model, first, the POLARIS agent-based modeling platform is used to predict changes in average daily traffic (ADT) under CAV scenario in the road network of Chicago metropolitan area as the dependent variable of the model. Second, a comprehensive set of variables and indicators representing network characteristics and urban structure patterns are generated. Finally, three machine learning techniques, namely, K-Nearest neighbors, Random Forest, and eXtreme Gradient Boosting, are used to characterize changes in ADT based on network characteristics under a CAV scenario. The estimated models are validated and are found to yield acceptable performance. In addition, SHapley Additive exPlanations (SHAP) analysis tool is employed to investigate the impact of important features on changes in ADT.

这项研究提出了一个模型，用于表征由于基于交通网络和内置环境特性而实施的联网自动驾驶汽车（CAV）技术导致的网络交通流量变化。为了开发这样的模型，首先，基于POLARIS代理的建模平台被用来预测在CAV场景下芝加哥都市圈的道路网络中作为模型变量的平均每日交通量（ADT）的变化。其次，生成了代表网络特征和城市结构模式的一组全面的变量和指标。最后，基于CAV场景下的网络特征，使用了三种机器学习技术，即K最近邻居，随机森林和极限梯度增强来表征ADT的变化。估计的模型经过验证，并得出可接受的性能。此外，SHapley Additive ExPlanations（SHAP）分析工具用于研究重要特征对ADT变化的影响。