JACIII Vol.12 No.2 pp. 106-110
doi: 10.20965/jaciii.2008.p0106


Using Automatic Calibration with Microscopic Traffic Simulation

Iisakki Kosonen

Helsinki University of Technology, 2100, 02015 TKK, Finland

May 31, 2007
September 12, 2007
March 20, 2008
traffic simulation, automatic calibration, error-function

The microscopic simulation is getting increasingly common in traffic planning and research because of the detailed analysis it can provide. The drawback of this development is that the calibration and validation of such a detailed simulation model can be very tedious. This paper summarizes the research on automatic calibration of a high-fidelity micro-simulation (HUTSIM) at the Helsinki University of Technology (TKK). In this research we used ramp operation as the case study. The automatic calibration of a detailed model requires a systematic approach. A key issue is the error-function, which provides a numeric value to the distance between simulated and measured results. Here we define the distance as combination of three distributions namely the speed distribution, gap distribution and lane distribution. We developed an automated environment that handles all the necessary operations. The system organizes the files, executes the simulations, evaluates the error and generates new parameter combinations. For searching of the parameter space we used a genetic algorithm (GA). The overall results of the research were good demonstrating the potential of using automatic processes in both calibration and validation of simulation models.

Cite this article as:
Iisakki Kosonen, “Using Automatic Calibration with Microscopic Traffic Simulation,” J. Adv. Comput. Intell. Intell. Inform., Vol.12, No.2, pp. 106-110, 2008.
Data files:
  1. [1] I. Kosonen, “HUTSIM – Urban Traffic Simulation and Control Model: Principles and Applications,” Doctoral thesis, Helsinki University of Technologies, Transportation Engineering Publications 100, Espoo, 1999.
  2. [2] F. Davidsson, I. Kosonen, and A. Gutowski, “TPMA-Traffic Performance on Major Arterials,” Royal Institute of Technology, Centre for Traffic Simulation Research, Stockholm, 2002.
  3. [3] T. Ma and B. Abdulhai, “Genetic algorithm-based optimization approach and generic tool for calibrating traffic microscopic simulation parameters,” Transportation Research Record No.1800, Transportation Research Board, pp. 6-15, Washington D.C., 2002.
  4. [4] D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning,” Addison-Wesley, Reading, Massachusset, 1989.
  5. [5] D. E. Goldberg, “Sizing Populations for Serial and Parallel Genetic Algorithms,” J. David Schaffer (Ed.), Proc. of the Third Int. Conf. on Genetic Algorithms, San Mateo, California, USA, Morgan Kaufmann Publishers, 1989.
  6. [6] C. A. Coello and G. T. Pulido, “Multiobjective Optimization using a Micro-Genetic Algorithm,” SECTOR Lee et al. (Eds.), Proc. of the Genetic and Evolutionary Computation Conf., San Francisco, California, USA, Morgan Kaufmann Publishers, 2001.
  7. [7] D. E. Goldberg and J. Richardson, “Genetic algorithms with sharing for multimodal function optimization,” J. J. Grefenstette (Ed.), Proc. of the Second Int. Conf. on Genetic Algorithms and their Application, Hillsdale, New Jersey, USA, Lawrence Erlbaum Associates, 1987.
  8. [8] K. Rasheed, “Guided crossover: A new operator genetic algorithm based optimization,” Proc. of the Congress on Evolutionary Computation, 1999.
  9. [9] L. Chabredier and R. David, “Programming book for HUTMAT,” Helsinki University of Technology, Laboratory of Transportation Engineering, Espoo, April, 2003.

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