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International Conference on Smart Cities and Green ICT Systems

International Conference on Vehicle Technology and Intelligent Transport Systems

SMARTGREENS 2018, VEHITS 2018: Smart Cities, Green Technologies and Intelligent Transport Systems pp 279–303Cite as

City-Level Agent-Based Multi-modal Modeling of Transportation Networks: Model Development and Preliminary Testing

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 992))

Abstract

Digital cities have the potential to produce major improvements in the transportation system with the eminent availability of data and the use of advanced modeling techniques. This modeling entails a number of challenges, namely: the network scale that covers large urban multi-modal transportation networks and the trade-off between model scalability and accuracy. This paper introduces a novel simulation framework that efficiently supports large-scale agent-based multimodal transportation system modeling. We call this framework “INTEGRATION Ver. 3.0”, or “INTGRAT3” for short. The INTGRAT3 framework utilizes both microscopic and mesoscopic modeling techniques to take advantage of the strengths of each modeling approach. In order to increase the model scalability, decrease the model complexity, and achieve a reasonable simulation speed, the INTGRAT3 framework utilizes parallel simulation through two partitioning techniques: spatial partitioning by separating the network geographically and vertical partitioning by separating the network by transportation mode for odes that interact minimally. The INTGRAT3 framework creates multimodal plans for a portion of the trips (controlled trips) and tracks the traveler’s trips on a second-by-second basis across the different modes. We instantiate this framework in a system model of Los Angeles (LA) supporting our study of the impact on transportation decisions over a 6 h period of the morning commute (6am–12pm). The results show that by modifying travel choices of only 5% of the trips, large reductions in traffic congestion (ranging from 3.8 to 17.2% reductions in vehicle delay) are achievable with marginal reductions in vehicle fuel consumption levels (ranging from 1.4 to 3.5%).

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Acknowledgements

This work was funded by the Advanced Research Projects Agency-Energy (ARPA-E) under award number DE-AR0000612.

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Authors and Affiliations

  1. Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA, 24060, USA

    Ahmed Elbery, Youssef Bichiou, Hesham A. Rakha & Jianhe Du

  2. Charles E. Via, Jr. Department of Civil and Environmental Enginering, Virginia Tech, Blacksburg, VA, 24060, USA

    Hesham A. Rakha

  3. Palo Alto Research Center (PARC), Palo Alto, USA

    Filip Dvorak & Matthew Klenk

Authors
  1. Ahmed Elbery
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  2. Youssef Bichiou
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  3. Hesham A. Rakha
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  4. Jianhe Du
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  5. Filip Dvorak
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  6. Matthew Klenk
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Corresponding author

Correspondence to Hesham A. Rakha .

Editor information

Editors and Affiliations

  1. School of Business, Maynooth University, Maynooth, Ireland

    Brian Donnellan

  2. CT RDA SSI, Siemens AG, Munich, Bayern, Germany

    Cornel Klein

  3. Dublin City University, Dublin, Ireland

    Markus Helfert

  4. Ford Research and Advanced Engineer, Dearborn, MI, USA

    Oleg Gusikhin

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Elbery, A., Bichiou, Y., Rakha, H.A., Du, J., Dvorak, F., Klenk, M. (2019). City-Level Agent-Based Multi-modal Modeling of Transportation Networks: Model Development and Preliminary Testing. In: Donnellan, B., Klein, C., Helfert, M., Gusikhin, O. (eds) Smart Cities, Green Technologies and Intelligent Transport Systems. SMARTGREENS VEHITS 2018 2018. Communications in Computer and Information Science, vol 992. Springer, Cham. https://doi.org/10.1007/978-3-030-26633-2_14

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  • DOI: https://doi.org/10.1007/978-3-030-26633-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26632-5

  • Online ISBN: 978-3-030-26633-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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