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

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外文摘要：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 inges-tion of traffic datasets at different resolutions to build spatiotemporal models for pre-dicting 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 repre-sented 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.

外文关键词：

Spatio-temporalData fusionGaussian processMultimodal dataAirport trafficO'Hare Airport

作者：

Damola M.Akinlana、Arindam Fadikar、Stefan M.Wild、Natalia Zuniga-Garcia、Joshua Auld

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作者单位：

Department of Mathematics and Statistics,University of South Florida,Tampa,FL 33620,USA

Decision and Infrastructure Sciences Division,Argonne National Laboratory,Lemont,IL 60439,USA

Applied Mathematics and Computational Research Division,Lawrence Berkeley National Laboratory,Berkeley,CA 94720,USA

Department of Industrial Engineering and Management Sciences,Northwestern University,Evanston,IL 60208,USA

Argonne National Laboratory,Energy Systems,Lemont,IL 60439,USA

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基金：

U.S.Department of Energy,Office of Vehicle TechnologiesArgonne,a U.S.Department of Energy Office of Science laboratory

项目编号：

DE-AC02-06CH11357DE-AC02-06CH11357

出版年：

2024

DOI：

10.1016/j.jtte.2023.11.006

交通运输工程学报(英文版)

CSTPCD

ISSN：2095-7564

年,卷(期)：2024.11(4)