Integrating Traffic Density Analysis with Automated Violation Detection Through IoT Sensor Fussion
DOI:
https://doi.org/10.25077/aijaset.v6i2.328Abstract
Urban traffic congestion imposes heavy economic and safety burdens on cities, especially in developing countries where fixed-time signal systems are used. Conventional methods are static and exacerbating delays and infractions. This study designs and implements a low-cost intelligent traffic control system that uses density-based signal processing and dual infrared sensors to adapt green-phase durations dynamically. It integrates real-time IoT-enabled offender detection and capture to link control and enforcement. The system uses a PIC16F877A microcontroller with optical dual-sensor arrays to measure traffic density and a Wi-Fi camera to capture images of violators. The Dynamic Time Allocation Technology (DTAT) system uses rules to figure out how much "green time," or 4 to 90 seconds, should be assigned to four different density states. MPLAB IDE is used for firmware development in C++, along with Proteus for simulation and real-world testing of prototypes. The prototype correctly identifies traffic state using a spatial sensor separation of 6 car lengths. It also adjusts the timing proportionally, reducing the number of wasted phases when conditions are uneven. Interrupt-driven enforcement reliably captures and transmits red-phase violations without disrupting control, demonstrating integrated feasibility on resource-limited hardware. This is a flexible, cost-effective approach to managing traffic in resource-poor areas.
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