On Urban Traffic Flow Benefits of Connected and Automated Vehicles

Ioannis Mavromatis, Andrea Tassi, Robert J. Piechocki, Mahesh Sooriyabandara

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Automated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and Automated Vehicles (CAVs), leveraging connectivity to further enhance perception via driving intention and sensor information sharing. Our investigation considers and quantifies the impact of each vehicle group in large urban road networks in Europe and in the USA. The key performance metrics are the traffic congestion, average speed and average trip time. Specifically, the numerical studies show that the traffic congestion can be reduced by up to a factor of four, while the average flow speeds of CAV group remains closer to the speed limits and can be up to 300% greater than the human-driven vehicles. Finally, traffic situations are also studied, indicating that even a small market penetration of CAVs will have a substantial net positive effect on the traffic flows.
Original languageEnglish
JournalThe 2020 IEEE 91st Vehicular Technology Conference: VTC2020-Spring
Early online date30 Apr 2020
Publication statusE-pub ahead of print - 30 Apr 2020

Bibliographical note

Accepted for IEEE VTC-Spring 2020, Antwerp, Belgium
The acceptance date for this record is provisional and based upon the month of publication for the article.

Keywords

  • physics.soc-ph

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