• Dr.Jyoti Kaushal
• Sanyam Upadhyay
• Bholeram Patidar
• Suraj Lohar

: As cities evolve into smarter ecosystems, traffic management remains one of the most pressing urban challenges, especially at intersections overwhelmed by unpredictable, multidirectional flow. Adaptive Urban Flow introduces a next-generation AI-powered traffic control system that senses, thinks, and adapts in real-time. At its core, the system fuses edge-based computer vision with deep neural networks to continuously interpret live traffic density from multiple directions. Layered on top is a self-optimizing reinforcement learning engine that dynamically recalibrates signal timings—not based on pre-set rules, but on live context and learned traffic behavior patterns. Unlike conventional traffic systems, Adaptive Urban Flow doesn't just react; it evolves. It predicts congestion before it happens, reshapes flow in real-time, and minimizes idle time at intersections, all while learning from every vehicle's movement. Initial simulations across urban intersection models have shown a remarkable reduction in wait times, emissions, and bottlenecks. This research not only pioneers a shift from static to sentient traffic management but also redefines how AI can reimagine urban mobility—making every green light count.

Traffic Density Estimation, Reinforcement Learning, Computer Vision, Intelligent Transportation Systems (ITS), Deep Learning, Multidirectional Traffic Optimization, Urban Mobility, Edge Computing, Traffic Signal Automation.

"Adaptive Urban Flow:An AI-Driven Real-Time Traffic Density Sensing System for Multidirectional Traffic Flow Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.467-471, June-2025, Available :http://www.jetir.org/papers/JETIRGW06077.pdf

"Adaptive Urban Flow:An AI-Driven Real-Time Traffic Density Sensing System for Multidirectional Traffic Flow Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. pp467-471, June-2025, Available at : http://www.jetir.org/papers/JETIRGW06077.pdf