An Embedded Fuzzy Logic Based Application for Density Traffic Control System

Adewale, Ajao Lukman; Jumoke, Ajao Falilat; Adegboye, Mutiu; Ismail, Abideen · 2018 · Crossref

DOI: 10.29099/ijair.v2i1.44

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Summary

This paper addresses the inefficiencies of conventional traffic control systems, which typically rely on fixed timing sequences or human agents that fail to adapt to real-time traffic density or prioritize emergency vehicles and pedestrians. The authors identify that existing automated systems often activate green lights for empty lanes while causing unnecessary delays for congested lanes or emergency responders. To resolve this, the study proposes an intelligent, embedded density-based traffic control system utilizing fuzzy logic to dynamically adjust signal timing based on lane occupancy and specific user priorities. The system was developed using an Arduino-based embedded platform featuring an ATmega328P microcontroller. The hardware design integrates two primary sensing mechanisms: Infrared (IR) sensors to detect vehicle presence and calculate lane density, and a siren detection sensor to identify emergency or security vehicles. The IR sensors operate based on the Beer-Lambert principle, where changes in light intensity correlate with the presence of vehicles. The software implementation combines C language programming in the Arduino IDE with a MATLAB-based fuzzy logic environment. The fuzzy controller processes inputs from the sensors to generate optimal timing sequences for the traffic lights, prioritizing lanes with higher vehicle density and granting immediate priority to emergency vehicles detected via the siren sensor. The system was simulated using Proteus Virtual Simulation Modelling before physical implementation. The results demonstrate that the fuzzy logic-based controller significantly improves traffic flow efficiency compared to traditional fixed-timing systems. The system achieved an average response time of 0.45 seconds, allowing for rapid adjustments to changing traffic conditions. By dynamically assigning waiting times based on actual lane density and emergency presence, the system effectively eliminates bottlenecks and reduces delays at traffic junctions. The integration of pedestrian and emergency considerations, which are often overlooked in prior automated systems, further enhances the safety and efficiency of road user management. The significance of this work lies in its contribution to the development of adaptive, intelligent transportation systems. By moving beyond static timing schedules, the proposed system offers a more responsive solution to urban traffic congestion, particularly in regions like West Africa where traffic management challenges are acute. The study highlights the effectiveness of combining fuzzy logic with embedded sensor technology to create a multifactor traffic control system that accounts for vehicle density, pedestrian needs, and emergency priorities, thereby reducing accident potential and improving overall traffic flow.

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• traffic density