Jun-Seok Oh

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Professor

Western Michigan University
Country flagUnited States

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About

Jun-Seok Oh is a Professor at Western Michigan University in the United States. His research focuses on pedestrian safety, automated feature recognition, and the analysis of transportation systems, as demonstrated in his recent articles. He has explored topics such as the impact of autonomous vehicles on pedestrian safety, injury outcomes in rural crashes, and the trends in micro-mobility injuries. His work applies advanced methodologies including machine learning and natural language processing to improve transportation safety and efficiency.

Recent Grants

Grant: Close

An Evaluation of Michigan Continuous Count Station Distribution

Open Date: 2016-03-01

Close Date: 2018-04-01

Grant: Close

Evaluating the Impacts of Speed Limit Changes on Identified Case Studies

Open Date: 2016-03-01

Close Date: 2017-12-01

Articles (17)

Year:

2025

Year:

2025

Recently, there has been a large increase in fatal pedestrian crashes that occur at night in the United States of America. In the state of Michigan, for the past 11 years (2011–2021), 76% of all fatal pedestrian crashes occurred at night. With the Toward Zero Death national strategy, identifying the presence of disparities by population characteristics in pedestrian nighttime crashes will enable better allocation of resources to the overrepresented segments of the population and enable the implementation of focused countermeasures to improve the safety of everyone. This study, therefore, aims to investigate the racial and poverty status disparities associated with pedestrian nighttime crashes in Michigan. Pedestrian nighttime crashes from 2011 to 2021 were aggregated at a census tract level. Demographic, socioeconomic, and built environment data were included at a census tract level. The negative binomial model was used to identify whether the racial makeup and economic status of census tracts are significantly associated with the increase in pedestrian nighttime crashes. Results show that census tracts where the majority of people are Black are associated with a 20% increase in pedestrian nighttime crashes. Moreover, poor tracts are associated with a 40% increase in pedestrian nighttime crashes. It was revealed that crashes recorded by police officers as “dark unlighted” are concentrated more in poor census tracks. Also, dark unlighted crashes were more concentrated in a census tract where a majority of the residents identify as Black compared with the majority of residents who identify as White. The use of LED lighting is recommended to improve pedestrian visibility at night.

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2024

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2024

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2024

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2024

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2024

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2022

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2022

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2021

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2021

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2020

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2020

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2020

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2020

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2020

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2020

Collaborators (4)

Valerian Kwigizile

Professor

Western Michigan University

UNITED STATES
View details

Ron Van Houten

Western Michigan University

UNITED STATES
View details

Norran Novat

Graduate Teaching and Research assistant

Western Michigan University

UNITED STATES
View details

Zachary Asher

Assistant Professor

Western Michigan University

UNITED STATES
View details
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