The fundamental diagram of autonomous vehicles: Traffic state estimation and evidence from vehicle trajectories


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Date

2025-12

Publication Type

Journal Article

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yes

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Abstract

The fundamental diagram (FD) is a key tool in traffic flow theory, describing the relationship between traffic flow and density at the link level. Traditionally, FD estimation relies on data from static sensors, although vehicle trajectory data provides an alternative approach. Driver heterogeneity strongly influences the shape and scatter of the FD and is crucial for traffic management. Autonomous vehicles (AVs), exhibiting distinct driving behavior from human drivers, are expected to alter the FD. However, limited observations of AVs in stationary conditions have constrained research in this area. This study addresses this gap by introducing the platoon fundamental diagram (PFD), a simple method to infer empirical FDs from platoon trajectory data. PFD derives pseudo-states from vehicle trajectories and aggregates them to capture consistent relationships between flow, density, and speed—without requiring stationary conditions or backward wave speed estimation. The results highlight the impact of 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 applications using NGSIM and I–24 Motion datasets.

Publication status

published

Editor

Book title

Volume

5

Pages / Article No.

100212

Publisher

Elsevier

Event

Edition / version

Methods

Software

Geographic location

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Subject

Traffic flow; Fundamental diagram (FD); Vehicle trajectories; Connected and automated vehicles (AVs); Intelligent transportation systems

Organisational unit

08686 - Gruppe Strassenverkehrstechnik check_circle
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG

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