From zero research experience to IEEE-published author as a 9th grader
Aarav built an AI framework that predicts traffic incident likelihood, expected waiting time and CO2 emissions from static traffic scene images. YOLO object detection counts and classifies vehicles, and those features feed a Random Forest regressor for multi-target prediction. The paper was published in IEEE Xplore through ICITSIF 2026 in Indore, India, May 29 to 30, 2026.
AI & Intelligent Transportation
Published in IEEE Xplore
IEEE ICITSIF 2026, 2026
Aarav Brahmbhatt, YRI Fellow
A 9th grader at The Wardlaw-Hartridge School interested in AI, with no research or publication experience.
IEEE-published author with an AI traffic prediction framework accepted at an international conference.
“I wanted to create something that not only detects traffic, but also uses the information to predict future problems. My YOLO-based traffic prediction system got accepted to IEEE ICITSIF 2026, all as a 9th grader.”
Published in IEEE Xplore, DOI 10.1109/ICITSIF69060.2026.11609117
Presented at the 1st International Conference on Intelligent Technologies for a Sustainable and Inclusive Future
Built a two-stage YOLO plus Random Forest pipeline for multi-target traffic prediction
Predicts incident likelihood, waiting time and CO2 emissions from a single image
Urban Traffic Incident Prediction via YOLO-based Detection and Random Forest ModelingRaeyaan Muppaneni, Published and presented at IEEE WcCST-2026