A platoon-centric approach to the capacity analysis of mixed traffic comprising connected and autonomous vehicles

The study of mixed traffic capacity, involving both Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs), remains a critical area of research. Traditional models have typically focused on individual vehicles, while this research shifts the focus to platoons as the fundamental units of analysis to better capture the platooning characteristics of CAVs. Specifically, we introduce a new metric, the Inter-Platoon Platooning Intensity (IPI), to facilitate the analysis of mixed traffic capacity. Through both mathematical and numerical investigations, we evaluate the impact of the proposed IPI and Maximum Platoon Size (MPS) on mixed traffic dynamics. Our findings indicate that: (1) the IPI effectively measures the clustering of CAVs in a single-lane mixed traffic environment; (2) the calculated mixed traffic capacity closely matches the actual traffic capacity, showing only minor deviations; (3) the marginal analysis demonstrates the conditions under which mixed traffic capacity correlates monotonically with either MPS or IPI; and (4) an optimal MPS is determined that maximizes mixed traffic capacity. These insights contribute significantly to the existing literature on the effects of platoon size and platooning intensity on mixed traffic flow. • A new metric, the Inter-Platoon Platooning Intensity (IPI), is introduced. • Impact of IPI and Maximum Platoon Size (MPS) is examined on mixed traffic dynamics. • IPI effectively measures the clustering strength of CAVs in mixed traffic. • Conditions where capacity rises monotonically with either MPS or IPI are derived. • The analysis is extended to the development of mixed traffic fundamental diagram.

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Publication Details

Journal
Transportation Research Part C Emerging Technologies
Published
2025-05-26
DOI
https://doi.org/10.1016/j.trc.2025.105170
Citations
1
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
1.21
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article

A platoon-centric approach to the capacity analysis of mixed traffic comprising connected and autonomous vehicles

Yiik Diew Wong, Feng Zhu, Peibo Zhao
1 citations
Transportation Research Part C Emerging Technologies
Traffic control and management
1.21
article

A platoon-centric approach to the capacity analysis of mixed traffic comprising connected and autonomous vehicles

Yiik Diew Wong, Feng Zhu, Peibo Zhao
article en
1 citations

Abstract

The study of mixed traffic capacity, involving both Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs), remains a critical area of research. Traditional models have typically focused on individual vehicles, while this research shifts the focus to platoons as the fundamental units of analysis to better capture the platooning characteristics of CAVs. Specifically, we introduce a new metric, the Inter-Platoon Platooning Intensity (IPI), to facilitate the analysis of mixed traffic capacity. Through both mathematical and numerical investigations, we evaluate the impact of the proposed IPI and Maximum Platoon Size (MPS) on mixed traffic dynamics. Our findings indicate that: (1) the IPI effectively measures the clustering of CAVs in a single-lane mixed traffic environment; (2) the calculated mixed traffic capacity closely matches the actual traffic capacity, showing only minor deviations; (3) the marginal analysis demonstrates the conditions under which mixed traffic capacity correlates monotonically with either MPS or IPI; and (4) an optimal MPS is determined that maximizes mixed traffic capacity. These insights contribute significantly to the existing literature on the effects of platoon size and platooning intensity on mixed traffic flow. • A new metric, the Inter-Platoon Platooning Intensity (IPI), is introduced. • Impact of IPI and Maximum Platoon Size (MPS) is examined on mixed traffic dynamics. • IPI effectively measures the clustering strength of CAVs in mixed traffic. • Conditions where capacity rises monotonically with either MPS or IPI are derived. • The analysis is extended to the development of mixed traffic fundamental diagram.

Transportation Research Part C Emerging TechnologiesVol. 177
Nanyang Technological University (SG)
Sustainable cities and communities
Openalex Percentile: Top 21%
Traffic control and management
1.21
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