Skip to content
Research Article Open access CC BY 4.0

An Automatic Traffic Violation Ticketing System Using Radio Frequency Identification (RFID) Technology

Shokenu Emmanuel Segun, Oke Alice Oluwafunke, Ogbewey Leonard Iyase, Elijah Aduragbemi Aransiola, Monsuru Abolade Adeagbo, Bakare Alimat Abisola, Babatunde Joshua Dada

Journal of Engineering Research and Reports · pp. 61–69 · Published 3 May 2023

10.9734/jerr/2023/v25i1870

Abstract

Travel convenience is impacted by a number of factors, including the condition of the road, traffic, length of trip, accidents, speed, etc. The daily increase in accident rates poses the biggest concern. These events not only result in fatalities but also increase the nation's financial losses. Traffic congestion is brought on by road users' lack of discipline and sentiments, which may result in traffic offenses. The goals of this research are to curb any traffic infractions as well as reduce corruption and nepotism on the road. This system was developed using an Atmega328 microcontroller, which instructs the system to identify any driver who violates a traffic light and send an SMS message to the violator and the road management of the violation and the location where it was done. This identification is done with the help of a radio frequency identification (RFID) reader, which gets the unique ID of the vehicle and sends back the information to the microcontroller, which now instructs the GSM module to send the SMS message.

Traffic violation radio frequency identification (RFID) tag radio frequency identification (RFID) module radio frequency identification (RFID) reader traffic signal GSM module power supply

Cited by 4

A Study on Automation of Traffic Violation Detection

Keesari Abhinav Reddy, Vanaparthi Sai Charan, Md. Sufiyan · Lecture Notes in Networks and Systems · 2025

Activity Based Travel Demanding Model Using Apache Kafka and Spark

Shweta Varshney, Rupa Rani, Tanya Gaur · 2024 2nd International Conference on Disruptive Technologies (ICDT) · 2024

Real time urban traffic prediction using RFID and a hybrid LSTM random forest model

Omar Khattab, B. Saravana Balaji, Fatmah Alghadhoori · Scientific Reports · 2025

Hybrid machine learning and computational fluid dynamics framework for optimizing supercritical CO₂ pipeline networks in large-scale carbon capture and storage

Abiola Samuel Ajayi, Ayoola David Bodude, Shokenu Emmanuel Segun · European Journal of Sustainable Development Research · 2026

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

4

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.