Bayesian Precalibration of a Large Stochastic Microsimulation Model.
IEEE Transactions on Intelligent Transportation Systems(2014)
摘要
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.
更多查看译文
关键词
Bayes methods,calibration,iterative methods,road traffic,stochastic processes,Bayesian precalibration,M40 motorway,automatic precalibration,emulator,iterative probabilistic precalibration,real-world traffic simulation model,stochastic microsimulator,stochastic traffic microsimulation model,Bayes statistics,Gaussian processes,calibration,microscopic simulation,traffic simulation
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要