Development of a Tool for an Efficient Calibration of CORSIM Models
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Development of a Tool for an Efficient Calibration of CORSIM Models

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English
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  • Creators:
  • Corporate Creators:
    University of Nevada, Las Vegas. Transportation Research Center
  • Corporate Contributors:
    Nevada. Dept. of Transportation
  • Subject/TRT Terms:
  • Publication/ Report Number:
    415-13-803
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    United States
  • TRIS Online Accession Number:
    01651449
  • Edition:
    Final Report
  • Corporate Publisher:
    Nevada. Dept. of Transportation
  • Abstract:
    This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of the search space and identifies a zone were a possible global solution can be located. After this zone has been found the simulated annealing algorithm refines the search and locates an optimal set of parameters within that zone. The design and implementation of this methodology seeks to enable the generalized calibration of microscopic traffic flow models. Two different CORSIM vehicular traffic systems were calibrated. All parameters after the calibration were within reasonable boundaries. The calibration methodology has been developed independently of the characteristics of the traffic flow models. Hence, it is likely to be easily used for the calibration of any other model. The proposed methodology has the capability to calibrate all model parameters considering multiple performance measures and time periods simultaneously. A comparison between the proposed MA and the SPSA algorithm was provided. The results are similar; however, the effort required to fine-tune the MA is considerably small compared to the SPSA. The running time of the MAbased calibration is larger compared to the SPSA. The MA still requires some knowledge of the model in order to set adequate optimization parameters. The perturbation of the parameters during the mutation process must be large enough to create a measurable change in the objective function but not too large to avoid noisy measurements.
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