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Efficient calibration techniques for large-scale traffic simulators
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DOI：10.1016/j.trb.2016.12.005.png)
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摘要
En 中文
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. We propose a metamodel approach. The analytical meta model 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. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
URBAN-TRANSPORTATION PROBLEMS
DESTINATION DEMAND ESTIMATION
SENSITIVITY-ANALYSIS
ASSIGNMENT MODELS
OPTIMIZATION
ALGORITHMS
FRAMEWORK
MITIGATION
PREDICTION
MATRICES
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6.3
论文数:
3.5K
被引数:
1.9W
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