Abhinav Parbhakar, Nancy Huynh, and Dr. Tae J. Kwon’s Paper Selected as a 2026 Editors’ Choice Article by CJCE

Congratulations to Abhinav Parbhakar, Nancy Huynh, and Dr. Tae J. Kwon for having their paper, “Multimodal Deep Learning Framework for Traffic Volume Estimation: A City-Scale Case Study in Edmonton, Alberta,” selected as one of the 2026 Editors’ Choice papers by the Canadian Journal of Civil Engineering.

The paper presents a multimodal deep learning framework for estimating annual average weekday traffic (AAWDT) across unmonitored road segments. Using aerial imagery and parametric transportation data, the study conducted a city-scale case study in Edmonton, Alberta, covering 749 ground-truth locations and more than 5,000 km of roadway. The proposed framework outperformed several established models, including linear regression, support vector regression, random forest, XGBoost, and neural networks, reducing estimation error by 7% to 85%. This work provides a scalable approach to support transportation agencies in city-wide traffic monitoring, planning, and infrastructure decision-making.

Find out more about the article here: https://doi.org/10.1139/cjce-2025-0547