Investigating the operational performance of connected and autonomous vehicles on signalized superstreets

ABSTRACT

With the development of artificial intelligence and wireless communication technology, connected and autonomous vehicles (CAVs) have been treated as a promising strategy to increase road capacity and mitigate traffic congestion. Besides the technology of CAVs, innovative intersection design was also originally introduced as a countermeasure for dealing with traffic congestion at intersections. Though many studies have been conducted to explore the benefits of CAVs under various transportation scenarios, few have been implemented to explore the impact of CAVs on traffic flow at innovative intersections. Hence, to achieve a better understanding of the impacts of CAVs on existing transportation infrastructure, this study conducts a simulation-based study to investigate the operational performance of CAVs with available Signal Phase and Timing (SPaT) information in the environment of typical innovative intersection design, i.e. superstreets. The impact of CAVs with different market penetration rates on the operational performance of a superstreet is identified. The operational performance of the superstreet increases as the market penetration rate increases overall. Average speed and average traffic delay for vehicles in the superstreet system can be improved with the increase of market penetration rates.

KEYWORDS:

• Connected and autonomous vehicles
• intersection design
• superstreets
• operational performance
• simulation
• market penetration rate

Acknowledgements

The authors express their deepest gratitude to the financial support by the U. S. Department of Transportation, University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) at The University of North Carolina at Charlotte (Grant Number: 69A3551747133). The authors confirm contributions to the paper as follows: study conception and design: Shaojie Liu; analysis and interpretation of results: Shaojie Liu; draft manuscript preparation: Shaojie Liu; research guidance: Wei (David) Fan; all authors reviewed the results and approved the final version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by U.S. Department of Transportation [grant number 69A3551747133].