Paper Contents
Abstract
ABSTRACTThe Real Time Smart Traffic Monitoring System is a scalable, intelligent urban traffic management solution designed to automate data collection, analysis, and dynamic control to optimize flow and enhance road safety. Built on an IoT enabled architecture with edge compute and cloud analytics layers, this system integrates inductive loop sensors, radar speed detectors, high resolution cameras, and microcontroller units (e.g., Raspberry PiArduino) to gather continuous real time metrics on vehicle count, speed, queue lengths, and environmental conditions.Leveraging AI driven computer vision models for incident and violation detection (e.g., accidents, red light running, speeding), along with reinforcement learning algorithms for adaptive signal timing, the system autonomously adjusts traffic lights to minimize delays and congestion. A solar powered design and low latency wireless communication (MQTT over LoRa5G) ensure reliable operation with minimal infrastructure dependencies. Through a user friendly mobile and web dashboard, stakeholders receive live traffic maps, predictive travel time estimates, and automated alerts, enabling data driven decision making and faster emergency response. The modular, cost effective platform supports seamless integration into existing smart city frameworks and paves the way for future expansions such as multi modal routing and remote traffic analytics.Keywords: Real Time Traffic Monitoring, IoT, AI Analytics, Dynamic Signal Control, Computer Vision, Adaptive Signal Timing, Smart City Mobility, Incident Detection.
Copyright
Copyright © 2025 Vikram Santosh Nimbalkar. This is an open access article distributed under the Creative Commons Attribution License.
