Trends, Topics, Leaders, Influential Studies, and Future Challenges of Machine Learning Studies in the Rail Industry-Reference-Cited by-同舟云学术

Trends, Topics, Leaders, Influential Studies, and Future Challenges of Machine Learning Studies in the Rail Industry

Published:2022-06 Issue:2 Volume:28 Page:
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Yong Gunwoo¹,Lee Ghang²^ORCID

Affiliation:

1. Graduate Research Assistant, Dept. of Architecture and Architectural Engineering, Yonsei Univ., A510, Bldg. 121, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.

2. Professor, Dept. of Architecture and Architectural Engineering, Yonsei Univ., A512, Bldg. 121, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29IS.1943-555X.0000691

Reference89 articles.

1. Applications of Artificial Intelligence in Transport: An Overview

2. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

3. Bridge Infrastructure Asset Management System: Comparative Computational Machine Learning Approach for Evaluating and Predicting Deck Deterioration Conditions

4. Prediction of arrival times of freight traffic on US railroads using support vector regression

5. Berger, R. 2020. World rail market study: Forecast 2020 to 2025. Brussels, Belgium: Union des Industries Ferroviaires Européennes.

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