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International Conference on Traffic and Granular Flow

TGF 2017: Traffic and Granular Flow '17 pp 97–106Cite as

Development of a Decision-Making Model for Merging Maneuvers: A Game Theoretical Approach

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Abstract

The development of advanced vehicle technologies will bring about new transportation system paradigms. In mixed traffic situations, where both connected automated vehicles (CAVs) and human drivers are present, it is important to first have a firm understanding of human driving behavior. Human driving behavior is complicated and will affect how CAVs need to operate. To come to this understanding, a realistic decision-making modeling framework for lane-changing behavior in human-driven vehicles is needed. Earlier, Kang and Rakha proposed a decision-making model for merging maneuvers at freeway on-ramps using a game theoretical approach (Kang and Rakha, Transp Res Rec J Transp Res Board 2623, 2017). To consider efficient integration within a microscopic traffic simulation modeling framework, this paper further develops the previously proposed model. The Next Generation SIMulation (NGSIM) dataset was used for model evaluation purpose. Validation results revealed that the developed model shows better predictability compared to the previous model.

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Acknowledgements

This research was funded partially by the Mid-Atlantic University Transportation Center (MAUTC) and a gift from the Toyota InfoTechnology Center.

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Authors and Affiliations

  1. Virginia Polytechnic Institute and State University, The Charles E. Via, Jr. Department of Civil and Environmental Engineering, Blacksburg, VA, USA

    Kyungwon Kang & Hesham A. Rakha

Authors
  1. Kyungwon Kang
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  2. Hesham A. Rakha
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Corresponding author

Correspondence to Hesham A. Rakha .

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Editors and Affiliations

  1. Dept. of Civil & Environ. Engineering, George Washington University, Washington, DC, USA

    Samer H. Hamdar

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Kang, K., Rakha, H.A. (2019). Development of a Decision-Making Model for Merging Maneuvers: A Game Theoretical Approach. In: Hamdar, S. (eds) Traffic and Granular Flow '17. TGF 2017. Springer, Cham. https://doi.org/10.1007/978-3-030-11440-4_12

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