Adaptive Traffic Management for Heterogeneous Urban Environments: A Case Study of Kanpur, Uttar Pradesh

Abstract With 70% of the global population projected to live in cities by 2050, Indian cities like Kanpur face escalating traffic congestion, pollution, and safety challenges, demanding innovative solutions to improve urban mobility. This survey explores Adaptive Traffic Management Systems (ATMS) in Kanpur, a 2.9-million-resident industrial hub, using real-time data to manage traffic efficiently. By integrating over 1000 traffic sensor records (e.g., 296 vehicles/hour moving at 15.36 km/h on NH-25), 50 Twitter alerts (e.g., 12 accidents at Naveen Market in 2024), and 4000 + weather reports (e.g., 4.65 mm rainfall on 2024-07-15), Kanpur’s AI-driven Adaptive Traffic Control System (ATCS) employs IoT devices, smart cameras, and GPS to adjust traffic signals dynamically, reducing delays by ~ 18% and congestion by ~ 30% at busy intersections like Ghantaghar. The system analyzes traffic patterns to identify bottlenecks, such as slow speeds (16.65 km/h) at Ganga Bridge, and uses social media and weather data to predict and prevent traffic jams. Visualizations, including time-series plots, scatter plots, heatmaps, and a colorful ATMS framework diagram, reveal congestion hotspots and system operations, providing clear insights into Kanpur’s traffic dynamics. Addressing unique challenges—monsoon slowdowns, festival surges (~ 2000 vehicles/hour), and railway disruptions at Ghantaghar—this study proposes a scalable, low-cost model for Indian smart cities. By incorporating 2025 technologies like AI, 5G, and blockchain, it aims to reduce emissions (contributing ~ 15% to global totals) and enhance urban livability, aligning with Kanpur’s Smart City Project, which has completed 68 of 72 planned initiatives, to foster sustainable urban mobility.