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

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...

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Bibliographic Details
Published in:Transportation letters 2021-11, Vol.13 (10), p.687-695
Main Authors: Parsa, Amir Bahador, Shabanpour, Ramin, Mohammadian, Abolfazl (Kouros), Auld, Joshua, Stephens, Thomas
Format: Article
Language:English
Subjects:
Connected and autonomous vehicles
machine learning
POLARIS
traffic flow
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Summary: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.
ISSN:1942-7867
1942-7875
DOI:10.1080/19427867.2020.1776956