Jun-Seok Oh
Engineering · Purdue University West Lafayette
Publications
229
Citations
2,759
Est. group size
—
Recurring co-author estimate
Active years
27
Publishing since 1999
Jun-Seok Oh's work centers on transportation engineering, with a focus on traffic safety analysis, pedestrian and micro-mobility crash patterns, autonomous vehicle interactions with pedestrians, and demand prediction for transit systems using machine learning and deep learning methods. Much of the research applies computational and data-mining techniques (e.g., neural networks, natural language processing) to real-world crash and traffic datasets to improve road safety and transportation planning. Note that the publication list attributed to this name includes some unrelated topics (e.g., animal science, history, computer vision), suggesting possible name overlap with other researchers in the underlying data.
Publication output declined from a peak of around 23 papers in 2018 to roughly 6-7 per year in 2021-2023, with a modest uptick to 8-10 in 2024-2025, indicating an overall slowing compared to a decade ago.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Comparative Analysis of AlexNet, ResNet-50, and VGG-19 Performance for Automated Feature Recognition in Pedestrian Crash Diagrams
Applied Sciences · 2025
- The Changes of bureaucrat promotion system in the Qin and Han Dynasties as seen through Gongling
CHUNGGUKSA YONGU (The Journal of Chinese Historical Researches) · 2025
- A Low-Cost Robotic Docking System with Monocular Camera and ArUco Markers<sup>*</sup>
2025
- Comparison of Enteric Ch4 Emissions between the Respiration Chamber and the Co2 Method in Holstein Heifers
SSRN Electronic Journal · 2025
- Advanced Demand Prediction for Demand-Responsive Transit (DRT) Systems: Integrating Graph Convolutional Networks and Deep Learning Techniques
2025
- Improvement of Wall Distance Pre-Classification Overset Grid Assembly and Its Applications
2025
- PSXIV-4 Effects of various sampling condition on CH4 emission measurement by the CO2 method in Hanwoo steers.
Journal of Animal Science · 2025
- The Effectiveness of eHMI Displays on Pedestrian–Autonomous Vehicle Interaction in Mixed-Traffic Environments
Sensors · 2024
- Micro-Mobility Safety Assessment: Analyzing Factors Influencing the Micro-Mobility Injuries in Michigan by Mining Crash Reports
Future Transportation · 2024
- Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool
The Open Transportation Journal · 2024
- Investigating Injury Outcomes of Horse-and-Buggy Crashes in Rural Michigan by Mining Crash Reports Using NLP and CNN Algorithms
Safety · 2024
- Investigating Racial and Poverty-Level Disparities Associated with Pedestrian Nighttime Crashes
Transportation Research Record Journal of the Transportation Research Board · 2024
- Impact of Slip Lanes on Pedestrian Safety at Roundabouts Considering Autonomous Vehicles
Transportation Research Record Journal of the Transportation Research Board · 2024
- Exact and efficient wall distance pre-classification overset grid assembly for unstructured grids
Journal of Computational Physics · 2024
- Exploring External Human Machine Interface Design for Autonomous Vehicle to Pedestrian Communication: Insights from Discussions and Drawing Sessions
AHFE international · 2024
- Journal of Transport & Health×12
- ScholarWorks - WMU (Western Michigan University)×10
- Transportation Research Record Journal of the Transportation Research Board×6
- Journal of Korean Society for Atmospheric Environment×4
- Accident Analysis & Prevention×3
- Andrew P. Tarko
Engineering · Purdue University West Lafayette
- Qiming Guo
Engineering · Purdue University West Lafayette
- Shoaib Mahmud
Engineering · Purdue University West Lafayette
- Sarah Hubbard
Engineering · Purdue University West Lafayette
- Mario Romero
Engineering · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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