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- Toledo, Tomer, et al.
(author)
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Calibration of Microscopic Traffic Simulation Models with Aggregate Data
- 2004
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In: CALIBRATION AND VALIDATION OF SIMULATION MODELS 2004. - : SAGE Publications. - 0309094704 ; , s. 10-19
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Conference paper (peer-reviewed)abstract
- A framework for the calibration of microscopic traffic simulation models using aggregate data is presented. The framework takes into account the interactions between the various inputs and parameters of the simulator by estimating origin-destination (O-D) flows jointly with the behavioral parameters. An optimization-based approach is used for the joint calibration. Since the calibration of the parameters depends on the estimated O-D flows and vice versa, the proposed framework is iterative. O-D estimation is based on the well-known generalized least squares estimator. A systematic search approach based on the complex algorithm is adopted for calibration of the behavioral parameters. This algorithm is particularly useful for the problem at hand since it does not require calculations of derivatives of the objective function. The applicability of the approach is demonstrated through its application to case studies using MITSIMLab, a microscopic traffic simulation model.
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| 2. |
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| 3. |
- Toledo, Tomer, et al.
(author)
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Statistical Validation of Traffic Simulation Models
- 2004
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In: CALIBRATION AND VALIDATION OF SIMULATION MODELS 2004. - : SAGE Publications. - 0309094704 ; , s. 142-150
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Conference paper (peer-reviewed)abstract
- Traffic simulation models support detailed analysis of the dynamics of traffic phenomena and are important tools for analysis of transportation systems. In order to evaluate correctly the impact of different traffic management schemes, simulation models must be able to replicate reality adequately. Model validation (i.e., the process of checking to what extent the model replicates reality) is discussed. The role of validation is defined within the scope of model development and calibration, and the framework for performing the validation is discussed. A hierarchy of statistical methods to validate different types of simulation outputs against observed data is examined. Also, a validation method is proposed on the basis of statistical tests on metamodels fitted to the observed and simulated data. A case study illustrates the applicability of the various methods.
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