IPIT researchers, including students, present a bevy of papers at national conference

Group shot of 7 people - 6 men and one woman - all looking at the camera.
Some of the students who presented research pose with scientist Yang Li, Professor Xiao Qin, and Assistant Professor Xiaowei Tom Shi, (third, fourth and fifth from left, respectively). They are (from left) Narayan Rai, Xiao Liang, Muhammad Fahad, and Mohammad Abrari Vajari.

Researchers and students in the college’s Institute for Physical Infrastructure & Transportation (IPIT) presented more than a dozen papers at the 2026 TRB Annual Meeting in Washington DC in January.

The Transportation Research Board, a program of the National Academies of Sciences, Engineering, and Medicine, hosts the world’s largest gathering of transportation researchers and practitioners at its annual meeting.

A man in a blue sports coat and a woman in a red sweater pose in front of a poster, both looking at the camera.
Professor Qin and student Joely Overstreet presented their work.

Faculty members from the college who co-authored the papers are: Xiao Qin, professor, and Xiaowei Tom Shi, assistant professor, civil & environmental engineering, and Susan McRoy, professor, and Tian Zhao, associate professor, computer science. Also represented is Robert Schneider, from UWM’s urban planning department.

The list of papers and their authors are:

  • 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.
  • 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.
  • 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 Trajectory Dataset of Pedestrian – Vehicle Interactions at Crosswalks via Deep Learning and Roadside Cameras.
  • 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.