首页|Researcher at New York University (NYU) Zeroes in on Machine Learning (Physics-I nformed Machine Learning for Calibrating Macroscopic Traffic Flow Models)
Researcher at New York University (NYU) Zeroes in on Machine Learning (Physics-I nformed Machine Learning for Calibrating Macroscopic Traffic Flow Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from New York Uni versity (NYU) by NewsRx correspondents, research stated, “Well-calibrated traffi c flow models are fundamental to understanding traffic phenomena and designing c ontrol strategies.” The news reporters obtained a quote from the research from New York University ( NYU): “Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physicsinformed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods. To this end, we combine the classical deep autoencod er, an unsupervised machine learning model consisting of one encoder and one dec oder, with traffic flow models. Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic pa rameters given flow and speed measurements. We also introduce the denoising auto encoder into our method so that it can handle not only with normal data but also corrupted data with missing values. We verified our approach with a case study of Interstate 210 Eastbound in California.”
New York University (NYU)CyborgsEmer ging TechnologiesMachine Learning
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