Calibrating the Local and Platoon Dynamics of Car-Following Models on the Reconstructed NGSIM Data

  • Valentina KurtcEmail author
  • Martin Treiber
Conference paper


The NGSIM trajectory data are used to calibrate two car-following models—the IDM and the FVDM. We used the I80 dataset which has already been reconstructed to eliminate outliers, non-physical data, and internal and platoon inconsistencies contained in the original data. We extract from the data leader-follower pairs and platoons of up to five consecutive vehicles thereby eliminating all trajectories that are too short or contain lane changes. Four error measures based on speed and gap deviations are considered. Furthermore, we apply three calibration methods: local or direct calibration, global calibration, and platoon calibration. The last approach means that a platoon of several vehicles following a data-driven leader is simulated and compared to the observed dynamics.


Next Generation Simulation (NGSIM) Platoon Full Velocity Difference Model (FVDM) Global Calibration Lane Change 
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  1. 1.
    Brockfeld, E., Kühne, R., Wagner, P.: Calibration and validation of microscopic traffic flow models. Transp. Res. Rec. J. Transp. Res Board 1876, 62–70 (2004)CrossRefGoogle Scholar
  2. 2.
    Ciuffo, B., Punzo, V.: Kriging meta-modelling in the verification of traffic micro-simulation calibration procedure. optimization algorithms and goodness of fit measures. In: TRB 90th Annual Meeting Compendium of Papers (2011)Google Scholar
  3. 3.
    FHWA, U.S.: Department of Transportation: ngsim: next generation simulation. Accessed 5 May 2007
  4. 4.
    Jiang, R., Wu, Q., Zhu, Z.: Full velocity difference model for a car-following theory. Phys. Rev. E 64(1), 017101 (2001)Google Scholar
  5. 5.
    Kesting, A., Treiber, M.: Calibrating car-following models by using trajectory data: methodological study. Transp. Res. Rec. J. Transp. Res. Board 2088, 148–156 (2008)CrossRefGoogle Scholar
  6. 6.
    Ossen, S., Hoogendoorn, S., Gorte, B.: Interdriver differences in car-following: a vehicle trajectory-based study. Transp. Res. Rec. J. Transp. Res. Board 1965, 121–129 (2006)CrossRefGoogle Scholar
  7. 7.
    Punzo, V.: A multistep procedure for vehicle trajectory reconstruction: application on the ngsim 180-1 datasetGoogle Scholar
  8. 8.
    Punzo, V., Ciuffo, B., Montanino, M.: May we trust results of car-following models calibration based on trajectory data? TRB 2012 Annu. Meet. (2012)Google Scholar
  9. 9.
    Punzo, V., Montanino, M., Ciuffo, B.: Do we really need to calibrate all the parameters? variance-based sensitivity analysis to simplify microscopic traffic flow models. IEEE Trans. Intell. Transp. Syst. 16(1), 184–193 (2015)CrossRefGoogle Scholar
  10. 10.
    Punzo, V., Simonelli, F.: Analysis and comparison of microscopic traffic flow models with real traffic microscopic data. Transp. Res. Rec. J. Transp. Res. Board 1934, 53–63 (2005)CrossRefGoogle Scholar
  11. 11.
    Ranjitkar, P., Nakatsuji, T., Asano, M.: Performance evaluation of microscopic traffic flow models with test track data. Transp. Res. Rec. J. Transp. Res. Board 1876, 90–100 (2004)CrossRefGoogle Scholar
  12. 12.
    Thiemann, C., Treiber, M., Kesting, A.: Estimating acceleration and lane-changing dynamics from next generation simulation trajectory data. Transp. Res. Rec. J. Transp. Res. Board 2088, 90–101 (2008)CrossRefGoogle Scholar
  13. 13.
    Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62(2), 1805 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.St. Petersburg Politechnic UniversitySaint PetersburgRussia
  2. 2.Technische Universtät DresdenDresdenGermany

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