文章基本信息

标题：Learning-based Traffic State Reconstruction using Probe Vehicles ⁎
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作者：John Liu ; Matthieu Barreau ; Mladen Čičić 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：2
页码：87-92
DOI：10.1016/j.ifacol.2021.06.013
语种：English
出版社：Elsevier
摘要：AbstractThis article investigates the use of a model-based neural network for the traffic reconstruction problem using noisy measurements coming from Probe Vehicles (PV). The traffic state is assumed to be the density only, modeled by a partial differential equation. There exist various methods for reconstructing the density in that case. However, none of them perform well with noise and very few deal with lagrangian measurements. This paper introduces a method that can reduce the processes of identification, reconstruction, prediction, and noise rejection into a single optimization problem. Numerical simulations, based either on a macroscopic or a microscopic model, show good performance for a moderate computational burden.
关键词：KeywordsModelingControlOptimization of Transportation SystemsFreeway Traffic ControlConnectedAutomated Vehicles