IPIT Researchers and Students Participated in the 105th TRB 2026 Annual Meeting and Presented Research

Researchers and students from IPIT attended the 105th Transportation Research Board (TRB) Annual Meeting in Washington, D.C., from January 11–15, 2026, presenting thirteen papers covering a range of critical transportation research topics. Throughout the conference, they engaged in discussions with researchers and practitioners across academia, government, and industry. The experience provided an excellent platform to showcase IPIT’s latest research, receive valuable feedback, and explore potential collaborative opportunities.
Presented Posters:
- Jung, S.*; Qin, X. Data-Driven Approach to Prioritizing Emergency Facility Deployment in High-Risk Freeway Tunnels.
- Schneider, R. J.*; Hemze, N.; Barbee, H.; Sveen, C.; Thorne, K.; Ogunniyi, O. E. Midblock Pedestrian Crossing Volumes and Crash Rates in Milwaukee, WI.
- Schneider, R. J.*; Gu, X.; Nelson, K.; Ferenchak, N. N.; Qin, X. Neighborhood-Level Shifts in Fatal and Severe Injury Pedestrian Crashes: 2008–2012 vs. 2017–2021.
- Fahad, M.; Tasnim, A.; Xiong, T.; Damaraju, A.; Zhao, T.; Qin, X.; Shi, X.* A Trajectory Dataset of Pedestrian – Vehicle Interactions at Crosswalks via Deep Learning and Roadside Cameras.
- Rai, N.; Liang, X.; Shen, D.; Huang, S.; Shi, X.*. Taming Stop-and-Go Traffic Shockwaves: Evidence from Radio-Controlled Car Experiments.
- Sadeghi Koupaei, A.; Shi, X.*. A Novel Surrogate Safety Measure Incorporating Detection and Communication Imperfections.
- Overstreet, J.; Qin, X.*; Parajuli, S.; Cherry, C.; Li, Y. Does Height Matter? An Analysis of Contributing Factors to Tall Vehicle–Pedestrian Crashes.
- Rukhsana, F.; Qin, X.*; Schneider, R. J. A Sequential Spatial-ML Framework for Interpretable Macro-Level Pedestrian Crash Modeling.
- Abrari Vajari, M.; Shi, X.; Qin, X.*. A Computer Vision Pipeline for Crosswalk Detection, Classification, and Quality Evaluation.
- Parajuli, S.; Cherry, C.; Overstreet, J.; Li, Y.; Qin, X.*. Impact of Tall Vehicles on Pedestrian Injury Severity Outcomes: Insights from Multi-State Pedestrian Crash Data.
- Devadiga, M.; Abrari Vajari, M.; McRoy, S. W.; Qin, X.*. Enhancing Data Accessibility Through Automated PII De-Identification in Crash Narratives.
- Fahad, M.; Tasnim, A.; Xiong, T.; Damaraju, A.; Zhao, T.; Qin, X.; Shi, X.*. A Comprehensive Evaluation Framework for Roadside Perception Systems.
- Liang, X.; Yang, C.; Shi, X.*. An Unsupervised Framework for Abnormal Driving Detection Using Utility-Based Feature Sequences.








