Interpretable machine learning for traffic congestion prediction: Unveiling the impact of different COVID-19 periods

Authors

Christopher J. Ng (ORCID: https://orcid.org/0000-0001-8056-6116)
Litian Xie (ORCID: https://orcid.org/0000-0001-5682-2853)
Yang Liu (ORCID: https://orcid.org/0000-0001-9259-8342)
Dan Zhu (ORCID: https://orcid.org/0000-0002-3365-6757)

Institutions

National University of Singapore (SG)

Publication Details

Journal: Communications in Transportation Research
Published: 2025-11-24
DOI: https://doi.org/10.1016/j.commtr.2025.100226
Primary Topic: Traffic Prediction and Management Techniques
Type: article
Field-Weighted Citation Impact: 0.00

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article

Interpretable machine learning for traffic congestion prediction: Unveiling the impact of different COVID-19 periods

Christopher J. Ng, Litian Xie, Yang Liu, Dan Zhu

Nov 24, 2025

Communications in Transportation Research

Traffic Prediction and Management Techniques

article

Interpretable machine learning for traffic congestion prediction: Unveiling the impact of different COVID-19 periods

Christopher J. Ng, Litian Xie, Yang Liu, Dan Zhu

November 24, 2025article en

Abstract

No abstract available for this paper.

Communications in Transportation ResearchVol. 5

National University of Singapore (SG)

Openalex Percentile: Top 61%

Traffic Prediction and Management Techniques

DOI Open Access PDF OpenAlex

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Interpretable machine learning for traffic congestion prediction: Unveiling the impact of different COVID-19 periods — Christopher J. Ng, Litian Xie, et al. · Communications in Transportation Research (2025) | TGRS Research Map | TGRS