Modeling Autonomous Vehicle-Targeted Aggressive Merging Behaviors in Mixed Traffic Environment

Jong In Bae; Michael P. Hunter; Angshuman Guin; Abhilasha Jairam Saroj; Wonho Suh

doi:10.1109/WSC60868.2023.10408181

Modeling Autonomous Vehicle-Targeted Aggressive Merging Behaviors in Mixed Traffic Environment

Jong In Bae, Michael P. Hunter, Angshuman Guin, Abhilasha Jairam Saroj, Wonho Suh

Applied Research for Mobility Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Promising advances in Autonomous Vehicle (AV) technology have fueled industry and research fields to dedicate significant effort to the study of the integration of AVs into the traffic network. This study focuses on the transition phase between all Human Driven Vehicles (HDVs) in the network to all AVs, where these different vehicle types coexist in a mixed traffic environment. This paper investigates the potential impacts of aggressive merging behaviors by human drivers on traffic performance in a mixed environment. For this, three vehicle types - AVs, HDVs, and Aggressive HDVs (AHDVs) - are modeled in an open-source microscopic traffic simulation model, SUMO. In the developed simulation, the AHDVs are modeled to emulate aggressive merging behaviors in front of AVs at a merge section of a freeway exit ramp. Several experiments are used to study the impact of such behavior. Results show travel time gains by AHDVs at the expense of AVs and HDVs.

Original language	English
Title of host publication	2023 Winter Simulation Conference, WSC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1570-1580
Number of pages	11
ISBN (Electronic)	9798350369663
DOIs	https://doi.org/10.1109/WSC60868.2023.10408181
State	Published - 2023
Event	2023 Winter Simulation Conference, WSC 2023 - San Antonio, United States Duration: Dec 10 2023 → Dec 13 2023

Publication series

Name	Proceedings - Winter Simulation Conference
ISSN (Print)	0891-7736

Conference

Conference	2023 Winter Simulation Conference, WSC 2023
Country/Territory	United States
City	San Antonio
Period	12/10/23 → 12/13/23

Funding

This manuscript has been co-authored the Georgia Institute of Technology under US Department of Transportation contracts and by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the USDOT and DOE Public Access Plans (https://www.bts.gov/ntl/public-access, https://energy.gov/downloads/doe-public-access-plan). The information, data, or work presented here was funded in part by the Georgia DOT and the Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE), a UTC funded by the USDOT. This work was supported by the Georgia DOT under Grant RP 18-33; and STRIDE under Grant G3.

Funders	Funder number
STRIDE
U.S. Department of Energy
U.S. Department of Transportation	G3, RP 18-33
Georgia Institute of Technology
Georgia Department of Transportation
Southeastern Transportation Research, Innovation, Development and Education Center
UT-Battelle	DE-AC05-00OR22725

Access to Document

10.1109/WSC60868.2023.10408181

Cite this

Bae, J. I., Hunter, M. P., Guin, A., Saroj, A. J., & Suh, W. (2023). Modeling Autonomous Vehicle-Targeted Aggressive Merging Behaviors in Mixed Traffic Environment. In 2023 Winter Simulation Conference, WSC 2023 (pp. 1570-1580). (Proceedings - Winter Simulation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC60868.2023.10408181

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title = "Modeling Autonomous Vehicle-Targeted Aggressive Merging Behaviors in Mixed Traffic Environment",

abstract = "Promising advances in Autonomous Vehicle (AV) technology have fueled industry and research fields to dedicate significant effort to the study of the integration of AVs into the traffic network. This study focuses on the transition phase between all Human Driven Vehicles (HDVs) in the network to all AVs, where these different vehicle types coexist in a mixed traffic environment. This paper investigates the potential impacts of aggressive merging behaviors by human drivers on traffic performance in a mixed environment. For this, three vehicle types - AVs, HDVs, and Aggressive HDVs (AHDVs) - are modeled in an open-source microscopic traffic simulation model, SUMO. In the developed simulation, the AHDVs are modeled to emulate aggressive merging behaviors in front of AVs at a merge section of a freeway exit ramp. Several experiments are used to study the impact of such behavior. Results show travel time gains by AHDVs at the expense of AVs and HDVs.",

author = "Bae, {Jong In} and Hunter, {Michael P.} and Angshuman Guin and Saroj, {Abhilasha Jairam} and Wonho Suh",

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year = "2023",

doi = "10.1109/WSC60868.2023.10408181",

language = "English",

series = "Proceedings - Winter Simulation Conference",

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Bae, JI, Hunter, MP, Guin, A, Saroj, AJ & Suh, W 2023, Modeling Autonomous Vehicle-Targeted Aggressive Merging Behaviors in Mixed Traffic Environment. in 2023 Winter Simulation Conference, WSC 2023. Proceedings - Winter Simulation Conference, Institute of Electrical and Electronics Engineers Inc., pp. 1570-1580, 2023 Winter Simulation Conference, WSC 2023, San Antonio, United States, 12/10/23. https://doi.org/10.1109/WSC60868.2023.10408181

Modeling Autonomous Vehicle-Targeted Aggressive Merging Behaviors in Mixed Traffic Environment. / Bae, Jong In; Hunter, Michael P.; Guin, Angshuman et al.
2023 Winter Simulation Conference, WSC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1570-1580 (Proceedings - Winter Simulation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Suh, Wonho

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AB - Promising advances in Autonomous Vehicle (AV) technology have fueled industry and research fields to dedicate significant effort to the study of the integration of AVs into the traffic network. This study focuses on the transition phase between all Human Driven Vehicles (HDVs) in the network to all AVs, where these different vehicle types coexist in a mixed traffic environment. This paper investigates the potential impacts of aggressive merging behaviors by human drivers on traffic performance in a mixed environment. For this, three vehicle types - AVs, HDVs, and Aggressive HDVs (AHDVs) - are modeled in an open-source microscopic traffic simulation model, SUMO. In the developed simulation, the AHDVs are modeled to emulate aggressive merging behaviors in front of AVs at a merge section of a freeway exit ramp. Several experiments are used to study the impact of such behavior. Results show travel time gains by AHDVs at the expense of AVs and HDVs.

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Modeling Autonomous Vehicle-Targeted Aggressive Merging Behaviors in Mixed Traffic Environment

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