Dongwoo Lee

Assistant Professor

Incheon National University

South Korea

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About

Dr. Dongwoo Lee is an Assistant Professor at Incheon National University in South Korea. His research areas encompass urban traffic safety, machine learning applications in roadway infrastructure, and urban behavioral dynamics. He has recently published articles on topics such as spatiotemporal analysis of urban vitality, road safety prediction modeling, and the impact of commercial vehicle safety training.

Articles (13)

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2025

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2025

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2024

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2024

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2023

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2023

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2023

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2023

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2021

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2021

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2021

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2021

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2020

Collaborators (4)

Anton Rozhkov

Industry Assistant Professor

New York University

UNITED STATES

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Abolfazl Mohammadian

Professor and Department Head

University of Chicago

UNITED STATES

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Sybil Derrible

University of Chicago

UNITED STATES

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tae-young heo

Chungbuk National University

SOUTH KOREA

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Dongwoo Lee

Research Interests

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Articles (13)

Identifying the Roadway Infrastructure Factors Affecting Road Accidents Using Interpretable Machine Learning and Data Augmentation

Spatiotemporal urban traffic safety analytical framework by integrating nonparametric approaches

Exploring road safety through urban fabric characteristics and theory-driven prediction modeling with SEM-XGBoost

Exploring the Spatiotemporal Heterogeneities in Urban Vitality Through Scalable Proxies from Mobile Data

Estimation of the Effect of Commercial Vehicle Driving Safety Training Using the Propensity Score Matching Method

Time Series Modeling Pipeline for Urban Behavioral Demand Prediction under Uncertainty

Interrelationships between urban travel demand and electricity consumption: a deep learning approach

Development of a City-wide Traffic Accident Prediction Model Using Hybrid Machine-based Learning Approaches

Analyzing travel behavior in Hanoi using Support Vector Machine

Interdependencies of Urban Behavioral Dynamics Whilst COVID-19 Spread

Effectiveness of Green Infrastructure Location Based on a Social Well-Being Index

Development of a machine learning-based sketch planning model for predicting mobile emissions

Predicting Residential Water Demand with Machine-Based Statistical Learning

Collaborators (4)

Anton Rozhkov

Abolfazl Mohammadian

Sybil Derrible

tae-young heo

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