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Efficient calibration techniques for large-scale traffic simulators
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Transportvetenskap.
2017 (engelsk)Inngår i: Transportation Research Part B: Methodological, ISSN 0191-2615, E-ISSN 1879-2367, Vol. 97, s. 214-239Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
PERGAMON-ELSEVIER SCIENCE LTD , 2017. Vol. 97, s. 214-239
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URN: urn:nbn:se:kth:diva-205492DOI: 10.1016/j.ijindorg.2016.12.005ISI: 000396960000011Scopus ID: 2-s2.0-85012297118OAI: oai:DiVA.org:kth-205492DiVA, id: diva2:1098423
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QC 20170524

Tilgjengelig fra: 2017-05-24 Laget: 2017-05-24 Sist oppdatert: 2020-05-11bibliografisk kontrollert

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Totalt: 29 treff
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  • de-DE
  • en-GB
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