Ai-Enhanced IoT for Smart City Traffic Management Through Data Analytics

Abstract

Smart cities have emerged over the past few decades to use technology to improve urban infrastructure, especially transportation. Traffic management previously used manual control systems like traffic signals driven by timers or sensors, which were ineffective at reacting to real-time conditions like congestion or accidents. AI-Enhanced IoT for Smart City Traffic Management optimizes traffic flow, reduces congestion, and improves transportation efficiency using AI and IoT devices. This research predicts traffic patterns, dynamically adjusts signal timings, reroutes vehicles, and prevents bottlenecks to improve traffic management decisions. Traditional road traffic management systems used static timers, basic sensor-based signal control, and human interventions, which caused poor traffic flow management and peak-hour delays. Before AI, traffic management struggled to use real- time data, respond to rapid traffic changes, and predict congestion based on current traffic patterns. Traffic congestion is a major urban issue as cities grow and vehicle numbers rise, causing lost productivity, higher pollution, and commuter annoyance. The demand for smarter, more responsive transportation systems to alleviate congestion, air pollution, and public safety drives this research. The suggested traffic control system uses AI and IoT for real-time monitoring and decision- making. The AI models can predict traffic jams, accidents, and peak congestion times by analysing live data from IoT sensors like traffic cameras, GPS data from vehicles, and road sensors. They can dynamically adjust traffic signal timings, reroute vehicles to less congested roads, and communicate with autonomous vehicles to optimize traffic flow. AI allows continuous traffic pattern learning, enabling adaptive and proactive traffic control, improving urban transportation efficiency. This technology decreases traffic congestion, travel times, and human involvement, making urban mobility management more sustainable.
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