Adedolapo Ogungbire

This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.

Researcher

University of Arkansas

Unknown Researcher

2 h-index 6 pubs 13 cited

Research Areas

Links

ORCID Lab Website

OverviewAI-generated summary

Adedolapo Ogungbire's research focuses on the application of machine learning and artificial intelligence techniques to address complex societal and transportation-related challenges. Much of this work investigates patterns and forecasting in areas such as workforce needs for state transportation agencies and the impact of major events like the COVID-19 pandemic on transportation usage. Ogungbire has explored telecommuting patterns and ridesourcing behavior during the pandemic, specifically examining data from Chicago, Illinois. Their work also extends to real-time applications, such as developing systems for helmet violation detection using advanced learning models. Ogungbire has published work on hyperparameter tuning in machine learning and comparative analyses of deep learning and statistical time series models for data-driven forecasting.

Metrics

h-index: 2
Publications: 6
Citations: 13

Selected Publications

Data-driven workforce forecasting for transportation infrastructure: A comparative analysis of deep learning and statistical time series models (2026)

Transportation Research Part E Logistics and Transportation Review DOI OpenAlex
Who gets what and sends it back: Online delivery and return patterns in the U.S. (2026)

Research in Transportation Business & Management DOI OpenAlex
Unlocking telecommuting patterns before, during, and after the COVID-19 pandemic: An explainable AI-driven study (2024)

Transportation Research Interdisciplinary Perspectives 2 citations DOI OpenAlex
Hyperparameter Tuning in Machine Learning: A Comprehensive Review (2024)

Journal of Engineering Research and Reports 186 citations DOI OpenAlex
Workforce forecasting for state transportation agencies: A machine learning approach (2024)

International Journal of Transportation Science and Technology 5 citations DOI OpenAlex
From stay-at-home to reopening: A look at how ridesourcing fared during the COVID-19 pandemic in Chicago, Illinois (2023)

Sustainable Cities and Society 6 citations DOI OpenAlex
Examining the Impact of the COVID-19 Pandemic on Ridesourcing Usage: A Case Study of Chicago (2022)

Research Square DOI OpenAlex