About Kaliprasana Muduli

A Postdoctoral Fellow in the Department of Civil and Environmental Engineering at the University of Alberta, working with Dr. Tae J. Kwon in the GeoTrans Lab. He received his Ph.D. in Transportation Engineering from the Indian Institute of Technology Roorkee (IIT Roorkee), India. Prior to joining the University of Alberta, he was a National Post-Doctoral Fellow supported by the Anusandhan National Research Foundation (ANRF), Government of India, at the Indian Institute of Technology Kharagpur. He also served as a Visiting Scholar at Purdue University, USA.

His research focuses on transportation safety, intelligent transportation systems, artificial intelligence, computer vision, and network-level road analytics. His work involves data-driven methods for understanding road-user behaviour and interactions, assessing transportation safety and infrastructure conditions, and developing intelligent transportation technologies for safer and more efficient mobility systems.

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Education

• Ph.D. in Transportation Engineering, Indian Institute of Technology Roorkee (IIT Roorkee), 2025

• M.Tech. in Transportation Engineering, Indian Institute of Technology Bhubaneswar (IIT Bhubaneswar), 2020

Research Interests

• Transportation Safety and Vulnerable Road Users 

• Intelligent Transportation Systems (ITS)

• Artificial Intelligence and Data Analytics in Transportation 

• Road-User Behaviour, Interaction, and Mobility Analytics 

• Network-Level Road Analytics and Infrastructure Condition Monitoring

Awards/Scholarships

• Best Doctoral Thesis Award, Conference on Transportation Systems Engineering and Management (CTSEM), 2026

• National Post-Doctoral Fellowship (N-PDF), Anusandhan National Research Foundation (ANRF), Government of India, 2025

• Selected Invitee, Sakura Science Exchange Program (SSP), Japan Science and Technology Agency (JST), Japan, 2025

• First Position in Poster Presentation, Young Researchers Conclave, CSIR-CRRI, New Delhi, 2023

• IIT Roorkee Alumni Travel Grant, 2024

Publications

• Muduli, K., & Ghosh, I. (2026). Understanding and predicting rolling-gap pedestrian behavior under mixed traffic using pose-informed deep learning. Traffic Injury Prevention, 1–10. https://doi.org/10.1080/15389588.2026.2718497

• Muduli, K., Panwar, A., & Ghosh, I. (2026). A comprehensive single-camera deep learning framework for detecting cracks and potholes, classifying severity, and quantifying pothole dimensions. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981261465419.

• Muduli, K., & Ghosh, I. (2026). A dynamic conflict-analysis framework for behaviour-centric safety evaluation of pedestrian–vehicle interactions at unsignalized crosswalks. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2026.108484

• Muduli, K., Ghosh, I., & Ukkusuri, S. V. (2026). A graph-based spatio-temporal framework for predicting safety-critical pedestrian–vehicle interactions at unsignalized crosswalks. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2026.108409

• Muduli, K., & Ghosh, I. (2026). Predicting pedestrian–vehicle interaction severity in mixed traffic conditions: Feature-based long short-term memory neural network approach. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981251364829

• Muduli, K., Maurya, A., & Ghosh, I. (2026). Single-frame machine learning approach to predict vehicular yielding intention while approaching a pedestrian crosswalk. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981251364833

• Sahu, D., Muduli, K., & Ghosh, I. (2025). Modeling of pedestrian crash frequency at unsignalized intersections using traffic conflict indicators. Canadian Journal of Civil Engineering. https://doi.org/10.1139/cjce-2024-0316

• Muduli, K., & Ghosh, I. (2025). Predicting pedestrian-vehicle interaction severity at unsignalized intersections. Traffic Injury Prevention. https://doi.org/10.1080/15389588.2024.2404713

• Muduli, K., & Ghosh, I. (2025). Identifying pedestrian-vehicle conflicts: An anomaly-detection approach with traffic conflict indicators. Lecture Notes in Civil Engineering. https://doi.org/10.1007/978-981-97-9943-5_9

• Muduli, K., & Ghosh, I. (2024). Prediction of vehicular yielding intention while approaching a pedestrian crosswalk. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981241252835

• Muduli, K., Sahu, V., & Ghosh, I. (2024). Predicting pedestrian movement in unsignalized crossings: A contextual cue-based approach. In 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE. https://doi.org/10.1109/COMSNETS59351.2024.10427345

• Muduli, K., & Ghosh, I. (2023). Prediction of the future state of pedestrians while jaywalking under non-lane-based heterogeneous traffic conditions. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981231161619