Bayesian precalibration of a large stochastic microsimulation model

Alexis Boukouvalas, Pete Sykes, Dan Cornford, Hugo Maruri-Aguilar

Research output: Contribution to journalArticlepeer-review


Calibration of stochastic traffic microsimulation models is a challenging task. This paper proposes a fast iterative probabilistic precalibration framework and demonstrates how it can be successfully applied to a real-world traffic simulation model of a section of the M40 motorway and its surrounding area in the U.K. The efficiency of the method stems from the use of emulators of the stochastic microsimulator, which provides fast surrogates of the traffic model. The use of emulators minimizes the number of microsimulator runs required, and the emulators' probabilistic construction allows for the consideration of the extra uncertainty introduced by the approximation. It is shown that automatic precalibration of this real-world microsimulator, using turn-count observational data, is possible, considering all parameters at once, and that this precalibrated microsimulator improves on the fit to observations compared with the traditional expertly tuned microsimulation.
Original languageEnglish
Pages (from-to)1337-1347
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number3
Early online date4 Mar 2014
Publication statusPublished - Jun 2014

Bibliographical note

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Funding: EPSRC - Managing Uncertainty in Complex Models Project under Grant D048893/1 and by the Aston Research Centre for Healthy Ageing.


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