Garima Jain

Work place: Computer Science and Engineering, Noida Institute of Engineering and Technology, Greater Noida, 201306, India

E-mail: garimajain@niet.co.in

Website:

Research Interests:

Biography

Garima Jain was born in Agra, India. She received the B.Tech. degree in computer science and engineering from Faculty of Engineering, Agra College, Agra, India, the M.Tech. degree in computer science from Galgotias College, Greater Noida, India, in 2017, and is currently pursuing the Ph.D. degree in artificial intelligence from Indira Gandhi Delhi Technical University for Women, Delhi, India. Her major field of study was machine learning and blockchain applications. Ms. Jain has eight years of academic experience and one year of industry experience. She is currently Deputy Head of the CSE Department at Noida Institute of Engineering and Technology, Greater Noida, India, where she leads curriculum development in AI/ML and blockchain. She has authored 4 SCIE-indexed journal papers, 25+ Scopus-indexed papers, 40+ conference papers, and six book chapters with IEEE, Springer, Elsevier, Wiley, and Taylor & Francis publishers. Her research interests include artificial intelligence, machine learning, blockchain in healthcare, and intelligent transportation systems.
Ms. Jain is a member of IEEE and Women in Engineering (WIE–IEEE). She serves as Core TPC Member for three IEEE conferences, reviewer for Scopus-indexed conferences, and editorial board member of three scientific journals. She received the Best Paper Award at an international conference and Best Project Award in the IDEAL Competition. Ms. Jain is a life member of the Indian Society for Technical Education (ISTE) and the International Society of Engineers, and a professional member of IEEE.

Author Articles
TRAC: PCU-Weighted Traffic Control with Virtual Lanes for Unstructured Indian Traffic

By Garima Jain Pranshu Bajaj Siddharth Dhingra Ankush Jain

DOI: https://doi.org/10.5815/ijisa.2026.05.03, Pub. Date: 8 Oct. 2026

Traffic congestion in Indian cities causes annual economic losses exceeding Rs 1.5 lakh crore. This paper proposes the TRAC (Traffic Routing and Allocation Control) system combining YOLOv3 object detection, virtual lane adaptation, and PCU-weighted scheduling for unstructured heterogeneous traffic. TRAC achieves 43.2s average waiting time across 1600 SUMO simulation cycles, reducing waiting time by 43% versus traditional methods (74.25s) and 27% versus actuated controllers (59s). Throughput increases 28% (1087 vs 862 vehicles per 10 minutes). The edge-deployable pipeline runs on NVIDIA Jetson Nano with 85ms end-to-end latency using a custom 12,500-frame Indian traffic dataset (0.87 mAP). Virtual lanes handle non-lane discipline while 8-class detection (car, bike, bus, rickshaw, etc.) enables accurate PCU weighting. This represents the first system combining PCU-weighted optimization with virtual lane adaptation specifically designed for chaotic Indian traffic conditions.

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