Efficient calibration techniques for large-scale traffic simulators

Road transportation simulators are increasingly used by transportation stakeholders around the world for the analysis of intricate transportation systems. Model calibration is a crucial prerequisite for transportation simulators to reliably reproduce and predict traffic conditions. This paper considers the calibration of transportation simulators. The methodology is suitable for a broad family of simulators. Its use is illustrated with stochastic and computationally costly simulators. The calibration problem is formulated as a simulation-based optimization (SO) problem. The authors propose a metamodel approach. The analytical metamodel combines information from the simulator with information from an analytical differentiable and tractable network model that relates the calibration parameters to the simulation-based objective function. The proposed algorithm is validated by considering synthetic experiments on a toy network. It is then used to address a calibration problem with real data for a large-scale network: the Berlin metropolitan network with over 24300 links and 11300 nodes. The performance of the proposed approach is compared to a traditional benchmark method. The proposed approach significantly improves the computational efficiency of the calibration algorithm with an average reduction in simulation runtime until convergence of more than 80%. The results illustrate the scalability of the approach and its suitability for the calibration of large-scale computationally inefficient network simulators.


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  • Accession Number: 01633915
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 1 2017 9:36AM