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Scalable Simulation-Optimization Method for Synthesizing Truck Tours from Trip Tables.

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Title: Scalable Simulation-Optimization Method for Synthesizing Truck Tours from Trip Tables.
Authors: de Camargo, Pedro Veiga1 (AUTHOR) pveigadecamargo@anl.gov, Zuniga-Garcia, Natalia1 (AUTHOR), Stinson, Monique2 (AUTHOR)
Source: Procedia Computer Science. 2026, Vol. 280, p630-637. 8p.
Subject Terms: Vehicle routing problem, Markov chain Monte Carlo, Freight & freightage, Computer simulation, Mathematical optimization, Automobile travel
Abstract: Medium- and heavy-duty vehicles play a central role in freight system performance, shaping congestion, energy use, and overall network efficiency. Accurately representing truck activity in transportation models is therefore essential for evaluating strategies that strengthen freight competitiveness. Many analyses require modeling truck operations as full-day tours, yet most regional models rely on origin–destination trip tables because tour-based models are costly and data-intensive to develop. This gap limits the ability of planners to assess routing behavior, vehicle duty cycles, or technology feasibility, all of which depend on tour-level information rather than isolated trips. This paper introduces a scalable simulation–optimization method that synthesizes realistic truck tours directly from trip tables for use in large-scale agent-based simulation frameworks. Stops are generated using a Markov chain Monte Carlo process derived from trip table transition probabilities, and tour sequences are optimized through a vehicle routing formulation calibrated with publicly available vehicle miles traveled data. The method preserves the aggregate structure of the input trip tables, enabling seamless integration into existing planning workflows with minimal development effort. Case studies in two metropolitan regions demonstrate that the approach produces realistic tour patterns with acceptable computational runtimes. [ABSTRACT FROM AUTHOR]
Database: Supplemental Index
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  – Url: https://www.doi.org/10.1016/j.procs.2026.04.080?
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An: 194227135
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PubType: Academic Journal
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PreciseRelevancyScore: 1060.75720214844
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  Data: Scalable Simulation-Optimization Method for Synthesizing Truck Tours from Trip Tables.
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  Data: <searchLink fieldCode="AR" term="%22de+Camargo%2C+Pedro+Veiga%22">de Camargo, Pedro Veiga</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pveigadecamargo@anl.gov</i><br /><searchLink fieldCode="AR" term="%22Zuniga-Garcia%2C+Natalia%22">Zuniga-Garcia, Natalia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stinson%2C+Monique%22">Stinson, Monique</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Procedia+Computer+Science%22">Procedia Computer Science</searchLink>. 2026, Vol. 280, p630-637. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Vehicle+routing+problem%22">Vehicle routing problem</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink><br /><searchLink fieldCode="DE" term="%22Freight+%26+freightage%22">Freight & freightage</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Automobile+travel%22">Automobile travel</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Medium- and heavy-duty vehicles play a central role in freight system performance, shaping congestion, energy use, and overall network efficiency. Accurately representing truck activity in transportation models is therefore essential for evaluating strategies that strengthen freight competitiveness. Many analyses require modeling truck operations as full-day tours, yet most regional models rely on origin–destination trip tables because tour-based models are costly and data-intensive to develop. This gap limits the ability of planners to assess routing behavior, vehicle duty cycles, or technology feasibility, all of which depend on tour-level information rather than isolated trips. This paper introduces a scalable simulation–optimization method that synthesizes realistic truck tours directly from trip tables for use in large-scale agent-based simulation frameworks. Stops are generated using a Markov chain Monte Carlo process derived from trip table transition probabilities, and tour sequences are optimized through a vehicle routing formulation calibrated with publicly available vehicle miles traveled data. The method preserves the aggregate structure of the input trip tables, enabling seamless integration into existing planning workflows with minimal development effort. Case studies in two metropolitan regions demonstrate that the approach produces realistic tour patterns with acceptable computational runtimes. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.procs.2026.04.080
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 8
        StartPage: 630
    Subjects:
      – SubjectFull: Vehicle routing problem
        Type: general
      – SubjectFull: Markov chain Monte Carlo
        Type: general
      – SubjectFull: Freight & freightage
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Automobile travel
        Type: general
    Titles:
      – TitleFull: Scalable Simulation-Optimization Method for Synthesizing Truck Tours from Trip Tables.
        Type: main
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          Name:
            NameFull: de Camargo, Pedro Veiga
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          Name:
            NameFull: Zuniga-Garcia, Natalia
      – PersonEntity:
          Name:
            NameFull: Stinson, Monique
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          Dates:
            – D: 15
              M: 03
              Text: 2026
              Type: published
              Y: 2026
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            – Type: volume
              Value: 280
          Titles:
            – TitleFull: Procedia Computer Science
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