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.