Calibrating car-following models by using trajectory data methodological study

Transportation Research Record

Volumn , Issue 2088, 2008, Pages 148-156

  Kesting, Arne ^a   Treiber, Martin ^a  

^a DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ERRORS; GENETIC ALGORITHMS; NONLINEAR PROGRAMMING;

CAR-FOLLOWING BEHAVIOR; CAR-FOLLOWING MODELING; CITY TRAFFIC; DATA SET; DIFFERENCE MODELS; INTELLIGENT DRIVER MODELS; METHODOLOGICAL STUDIES; OPTIMIZATION CRITERIA; TRAJECTORIES DATUM; VELOCITY DIFFERENCE;

TRAJECTORIES;

EID: 63849322364     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/2088-16     Document Type: Article

References (22)

1. Brockfeld, E., R. D. Kühne, and P. Wagner. Calibration and Validation of Microscopic Traffic Flow Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 1876, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 62-70.
2. Ranjitkar, P., T. Nakatsuji, and M. Asano. Performance Evaluation of Microscopic Flow Models with Test Track Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1876, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 90-100.
3. Punzo, V., and F. Simonelli. Analysis and Comparison of Microscopic Flow Models with Real Traffic Microscopic Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1934, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 53-63.
4. Ossen, S., and S. P. Hoogendoorn. Car-Following Behavior Analysis from Microscopic Trajectory Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1934, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 13-21.
5. Hoogendoorn, S., S. Ossen, and M. Schreuder. Adaptive Car-Following Behavior Identification by Unscented Particle Filtering. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007.
6. Deutsches Zentrum für Luft- und Raumfahrt. www.dlr.de/cs/. Accessed May 5, 2007.
7. Fouladvand, M. E., and A. H. Darooneh. Statistical Analysis of Floating-Car Data: An Empirical Study. European Physical Journal B, Vol. 47, 2005, pp. 319-328.
8. Panwai, S., and H. Dia. Comparative Evaluation of Microscopic Car-Following Behavior. IEEE Transactions on Intelligent Transportation Systems, Vol. 6, 2005, pp. 314-325.
9. Treiber, M., A. Hennecke, and D. Helbing. Congested Traffic States in Empirical Observations and Microscopic Simulations. Physical Review E, Vol. 62, 2000, pp. 1805-1824.
10. Jiang, R., Q. Wu, and Z. Zhu. Full Velocity Difference Model for a Car-Following Theory. Physical Review E, Vol. 64, 2001, p. 017101.
11. Bando, M., K. Hasebe, A. Nakayama, A. Shibata, and Y. Sugiyama. Dynamical Model of Traffic Congestion and Numerical Simulation. Physical Review E, Vol. 51, 1995, pp. 1035-1042.
12. Goldberg, D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass., 1989.
13. Treiber, M., A. Kesting, and D. Helbing. Delays, Inaccuracies and Anticipation in Microscopic Traffic Models. Physica A, Vol. 360, 2006, pp. 71-88.
14. Kesting, A., and M. Treiber. How Reaction Time, Update Time and Adaptation Time Influence the Stability of Traffic Flow. Computer-Aided Civil and Infrastructure Engineering, Vol. 23, 2008, pp. 125-137.
15. Treiber, M., A. Kesting, and D. Helbing. Influence of Reaction Times and Anticipation on the Stability of Vehicular Traffic Flow. In Transportation Research Record: Journal of the Transportation Research Board, No. 1999, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 23-29.
16. Green, M. How Long Does It Take to Stop? Methodological Analysis of Driver Perception-Brake Times. Transportation Human Factors, Vol. 2, 2000, pp. 195-216.
17. Ossen, S., and S. P. Hoogendoorn. Validity of Trajectory-Based Calibration Approach of Car-Following Models in the Presence of Measurement Errors. In Transportation Research Record: Journal of the Transportation Research Board, No. 2088, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 117-125.
18. Ossen, S., S. P. Hoogendoorn, and B. G. Gorte. Inter-Driver Differences in Car-Following: A Vehicle Trajectory Based Study. In Transportation Research Record: Journal of the Transportation Research Board, No. 1965, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 121-129.
19. NGSIM: Next Generation Simulation. FHWA, U.S. Department of Transportation. www.ngsim.fhwa.dot.gov. Accessed May 5, 2007.
20. Treiber, M., and D. Helbing. Memory Effects in Microscopic Traffic Models and Wide Scattering in Flow-Density Data. Physical Review E, Vol. 68, 2003, p. 046119.
21. Treiber, M., A. Kesting, and D. Helbing. Understanding Widely Scattered Traffic Flows, the Capacity Drop, and Platoons as Effects of Variance-Driven Time Gaps. Physical Review E, Vol. 74, 2006, p. 016123.
22. Hoogendoorn, S., S. Ossen, and M. Schreuder. Empirics of Multianticipative Car-Following Behavior. In Transportation Research Record: Journal of the Transportation Research Board, No. 1965, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 112-120.