Development of an Automated Traffic Decongestion Control System

Traffic congestion in urban and rural areas causes delays, safety hazards, and pollution. This study developed a traffic decongestion system using Arduino microcontroller, IR sensors, a servomotor, and an LCD display to reduce travel time, enhance safety, and support sustainable transportation. The system detects vehicles with IR sensors, controls barriers with a servomotor, and shows lane travel times on the LCD. By installing new traffic signals and using active traffic management strategies, the system was assessed through metrics like traffic flow and pedestrian safety. Results showed that the system effectively reduced travel times, improved safety, and supported sustainable transportation. Challenges were managed through thorough testing and communication. The study’s findings validate the effectiveness of such systems and offer insights for future traffic management research and practice.

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Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/nigercon62786.2024.10927086
Primary Topic
Wireless Sensor Networks and IoT
Type
article
Field-Weighted Citation Impact
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article

Development of an Automated Traffic Decongestion Control System

Samuel-Soma M. Ajibade, Adeolu Adesoji Adediran, Bamidele Segun Ilugbusi, Sunday A. Afolalu et al.
Wireless Sensor Networks and IoT
article

Development of an Automated Traffic Decongestion Control System

Samuel-Soma M. Ajibade, Adeolu Adesoji Adediran, Bamidele Segun Ilugbusi, Sunday A. Afolalu, Adedotun O. Adetunla, Adjugah Uvietobore Joshua
article en

Abstract

Traffic congestion in urban and rural areas causes delays, safety hazards, and pollution. This study developed a traffic decongestion system using Arduino microcontroller, IR sensors, a servomotor, and an LCD display to reduce travel time, enhance safety, and support sustainable transportation. The system detects vehicles with IR sensors, controls barriers with a servomotor, and shows lane travel times on the LCD. By installing new traffic signals and using active traffic management strategies, the system was assessed through metrics like traffic flow and pedestrian safety. Results showed that the system effectively reduced travel times, improved safety, and supported sustainable transportation. Challenges were managed through thorough testing and communication. The study’s findings validate the effectiveness of such systems and offer insights for future traffic management research and practice.

University of Johannesburg (ZA), Afe Babalola University (NG), Federal University Oye Ekiti (NG), Sunway University (MY)
Sustainable cities and communities
Openalex Percentile: Top 64%
Wireless Sensor Networks and IoT
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