O’Hare Airport Short-Term Ground Transportation Modal Demand Forecast Using Gaussian Processes-Reference-Cited by-同舟云学术

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O’Hare Airport Short-Term Ground Transportation Modal Demand Forecast Using Gaussian Processes

Published:2024-03 Issue:3 Volume:150 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Zuniga-Garcia Natalia¹^ORCID,Fadikar Arindam²^ORCID,Akinlana Damola M.³,Auld Joshua⁴

Affiliation:

1. Energy Systems, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439 (corresponding author). ORCID: .

2. Mathematics and Computer Science, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439. ORCID: .

3. Dept. of Mathematics and Statistics, Univ. of South Florida, College of Arts and Sciences Multidisciplinary Complex, Tampa, FL 33620.

4. Energy Systems, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-7918

Reference42 articles.

1. POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations;Auld J.;Transp. Res. Part C Emerging Technol.,2016

2. Network state estimation and prediction for real-time traffic management;Ben-Akiva M.;Networks Spatial Econ.,2001

3. Bhattacharya S. S. Phithakkitnukoon P. Nurmi A. Klami M. Veloso and C. Bento. 2013. “Gaussian process-based predictive modeling for bus ridership.” In Proc. 2013 ACM Conf. on Pervasive and Ubiquitous Computing Adjunct Publication UbiComp ’13 Adjunct. 1189–1198. New York: Association for Computing Machinery.

4. HetGP: Heteroskedastic Gaussian process modeling and sequential design in R;Binois M.;J. Stat. Software,2021

5. Gaussian process for nonstationary time series prediction;Brahim-Belhouari S.;Comput. Stat. Data Anal.,2004

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