A physics-informed machine learning for generalized bathtub model in large-scale urban networks-Reference-Cited by-同舟云学术

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A physics-informed machine learning for generalized bathtub model in large-scale urban networks

Published:2024-07 Issue: Volume:164 Page:104661
ISSN:0968-090X
Container-title:Transportation Research Part C: Emerging Technologies
language:en
Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Ka Eunhan^ORCID,Xue Jiawei^ORCID,Leclercq Ludovic,Ukkusuri Satish V.^ORCID

Publisher

Elsevier BV

Reference65 articles.

1. Perimeter and boundary flow control in multi-reservoir heterogeneous networks;Aboudolas;Transp. Res. B,2013

2. Departure time choice models in urban transportation systems based on mean field games;Ameli;Transp. Sci.,2022

3. Solving for equilibrium in the basic bathtub model;Arnott;Transp. Res. B,2018

4. Social optimum in the basic bathtub model;Arnott;Transp. Sci.,2022

5. Leaving the tub: The nature and dynamics of hypercongestion in a bathtub model with a restricted downstream exit;Bao;Transp. Res. Part E: Logist. Transp. Rev.,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach;Data Science for Transportation;2024-08-06

2. A bathtub model with nonlinear velocity–density relation;Physica A: Statistical Mechanics and its Applications;2024-08

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