• DocumentCode
    3568329
  • Title

    Parameter sampling strategies in traffic microsimulation

  • Author

    Punzo, Vincenzo ; Montanino, Marcello

  • Author_Institution
    Dept. of Civil, Environ. & Archit. Eng., Univ. of Naples Federico II, Naples, Italy
  • fYear
    2015
  • Firstpage
    45
  • Lastpage
    51
  • Abstract
    The paper investigates the impact of different sampling strategies of car-following and lane-changing model parameters on traffic simulation results. The investigation considered seven possible sampling strategies including sampling parameters from independent normal distributions, which is customarily in commercial simulation software. Study results revealed that model performances in case of sampling from normal pdfs are extremely poor. In turn, results proved that parameter correlation, as well as the parameter distribution model, entail a big impact on model performances and should be properly take into account in the microsimulation practice.
  • Keywords
    digital simulation; normal distribution; road traffic; traffic engineering computing; car-following model parameter; independent normal distributions; lane-changing model parameter; parameter correlation; parameter distribution model; parameter sampling strategies; sampling parameters; simulation software; traffic microsimulation; Calibration; Correlation; Data models; Software; Stochastic processes; Trajectory; Vehicles; calibration; driver heterogeneity; parameter sampling; traffic micro-simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015 International Conference on
  • Print_ISBN
    978-9-6331-3140-4
  • Type

    conf

  • DOI
    10.1109/MTITS.2015.7223235
  • Filename
    7223235