A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow

ABSTRACT

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.

KEYWORDS:

• Connected and autonomous vehicles
• POLARIS
• traffic flow
• machine learning

Acknowledgments

The authors gratefully acknowledge the sponsorship of the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program, managed by David Anderson of the Vehicle Technologies Office of the US Department of Energy. This study was conducted by the University of Illinois at Chicago and Argonne National Laboratory, a US Department of Energy laboratory managed by UChicago Argonne, LLC under Contract No. DE-AC02-06CH11357. The authors are solely responsible for the findings of this research which do not necessarily represent the views of the US Department of Energy or the United States Government.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Argonne National Laboratory.