Ankush Jain

Work place: Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, 110078, India

E-mail: ankush.jain@nsut.ac.in

Website:

Research Interests:

Biography

Dr. Ankush Jain is an Assistant Professor in the Department of Computer Science and Engineering at Netaji Subhas University of Technology (NSUT), Dwarka, New Delhi. He holds a Ph.D. from ABV-Indian Institute of Information Technology and Management (ABV-IIITM), Gwalior, where he also completed his M.Tech. in Advanced Networks. He earned his B.Tech. degree in Computer Science and Engineering from Rajasthan Technical University, Kota.
Dr. Jain has previously served as an Assistant Professor at Indira Gandhi Delhi Technical University for Women (IGDTUW), Delhi, and Bennett University, Greater Noida. He has authored several SCI-indexed journal articles and book chapters in reputed international publications. He is also an active reviewer for leading journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Computational Social Systems, IEEE Sensors Journal, Information Processing and Management, Expert Systems with Applications, Scientific Reports, Pattern Analysis and Applications, and the Journal of Machine Learning and Cybernetics (JMLC).
His research interests include computer vision, recommendation systems, evolutionary computation, pattern recognition, and machine learning. Dr. 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 and ACM.

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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