The impact of autonomous vehicles on urban traffic network capacity: an experimental analysis by microscopic traffic simulation

ABSTRACT

Urban commuters have been suffering from traffic congestion for a long time. In order to avoid or mitigate the congestion effect, it is significant to know how the introduction of autonomous vehicles (AVs) influence the road capacity . The effects that AVs bring to the macroscopic fundamental diagram (MFD) were investigated through microscopic traffic simulations. This is a key issue as the MFD is a basic model to describe road capacity in practical traffic engineering. Accordingly, the paper investigates how the different percentage of AVs affects the urban MFD. A detailed simulation study was carried out by using SUMO both with an artificial grid road network and a real-world network in Budapest. On the one hand, simulations clearly show the capacity improvement along with AVs penetration growth. On the other hand, the paper introduces an efficient modeling for MFDs with different AVs rates by using the generalized additive model (GAM).

KEYWORDS:

• Autonomous vehicles
• macroscopic fundamental diagram
• urban road
• capacity
• SUMO
• generalized additive model

Acknowledgments

The research work was supported by the Hungarian Government and co-financed by the European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00001: Talent Management in Autonomous Vehicle Control Technologies) and by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.