Characterisation of the impacts of autonomous driving on highway capacity in a mixed traffic environment: An agent-based approach

Abstract

The current planning and operational analysis tools utilised by researchers and practitioners for transportation facilities evaluation purposes are limited in their relevance to evaluate the future impact of connected and automated vehicles (CAVs). Since CAV technologies are becoming more prevalent on the market, the adaptation of the conventional methodologies to estimate highway performance is becoming essential to accommodate these technologies. This study tries to fill up this gap in the literature by formulating capacity analysis model estimate the potential impacts of CAVs on the capacity of highways at different market penetration (MP) levels of CAVs. The developed analytical model would present itself as a guideline to transportation agencies while developing CAV-based transportation infrastructure. Later, this study proposes an agent-based modelling and simulation framework to address the heterogeneity of drivers behaviour and the potential impact of CAVs on highway traffic performance under different MP levels. Results showed that deploying CAVs in the traffic stream has the potential to double the capacity of the highway corridor at high MP levels. The results show CAV operation and implementation readiness for both public agencies as well as state Departments of Transportation or private industries in the future. © The Institution of Engineering and Technology 2020.