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O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

Journal of Traffic and Transportation Engineering(English Edition) 2024年第4期11卷 721-732页

作者： Damola M.Akinlana arindam fadikar Stefan M.Wild Natalia Zuniga-Garcia Joshua Auld Department of Mathematics and Statistics University of South Florida Decision and Infrastructure Sciences Division Argonne National Laboratory Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Department of Industrial Engineering and Management Sciences Northwestern University Argonne National Laboratory Energy Systems Argonne National Laboratory Energy Systems Lemont

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process(GP) regression was employed to provide short-term forecasts using data collected by loop detectors(sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

关键词： Spatio-temporal Data fusion Gaussian process Multimodal data Airport traffic O’Hare Airport

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