A Data-Driven Approach to Characterize the Impact of Connected and Autonomous Vehicles on Traffic Flow

Abstract

The current study aims to present 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, POLARIS agent-based modeling platform is used to predict changes in average daily traffic (ADT) under CAVs 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. Three machine learning models namely K-Nearest neighbors, Random Forest, and eXtreme Gradient Boosting are developed and validated to establish the relationship between network characteristics and changes in ADT under CAVs scenario. The estimated models 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, which discloses the most important link properties, network features, and demographic information in predicting change in ADT under the analyzed CAVs scenario.