Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin-Reference-Cited by-同舟云学术

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Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin

Published:2024-07-06 Issue: Volume: Page:1-13
ISSN:1942-7867
Container-title:Transportation Letters
language:en
Short-container-title:Transportation Letters

Author:

Rahman Md Hishamur¹²^ORCID,Abrar Masnun³,Rifaat Shakil Mohammad⁴^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, The University of Utah, Salt Lake City, UT, USA

2. Department of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh

3. Department of Document Control, Tekken-AML-Abenikko JV, Dhaka, Bangladesh

4. Department of Civil and Environmental Engineering, Islamic University of Technology, Gazipur, Bangladesh

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19427867.2024.2372944

Reference38 articles.

1. Arjovsky, M., S. Chintala, and L. Bottou. 2017. “Wasserstein Generative Adversarial Networks.” International conference on machine learning, Sydney, Australia, 214–223.

2. Direct Ridership Model of Bus Rapid Transit in Los Angeles County, California

3. Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach

4. Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks

5. An analysis of Metro ridership at the station-to-station level in Seoul

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