What is it about?
The fundamental diagram (FD) is a key tool in traffic flow theory, describing the relationship between traffic flow and density at the link level. Essentially, it describes all the possible traffic states that can appear on a road. Autonomous vehicles (AVs) drive longitudinally, keeping contant time gap distance from the vehicle ahead, which leads to a particular FD different from human drivers. This work analyzes data and reveals the expected traffic performance of AVs.
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Why is it important?
It is the first work that reveals an almost complete fundamental diagram for AVs in traffic flow.
Perspectives
This work proposes the platoon fundamental diagram (PFD), a simple method to infer empirical FDs from platoon trajectory data. PFD can be used in non-stationary traffic conditions, exploiting the symmetry between vehicle acceleration and deceleration. The results highlight the impact of autonomous vehicles (AVs) on traffic flow capacity, driver heterogeneity, and oscillation propagation. Comparative analysis with human-driven experiments provides additional insights. Furthermore, the PFD's potential as a practical tool for traffic state estimation in mixed traffic conditions is demonstrated through real-world trajectory datasets.
Michail Makridis
ETH Zürich, D-CHAB
Read the Original
This page is a summary of: The fundamental diagram of autonomous vehicles: Traffic state estimation and evidence from vehicle trajectories, Communications in Transportation Research, December 2025, Tsinghua University Press,
DOI: 10.1016/j.commtr.2025.100212.
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