INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 138
Next-Gen Smart Traffic Violation Detection Using Edge AI and IoT
for Safer Urban Mobility
Bharati Amit Patil*, Shubhangi Ghule
Department of Computer Science, Dr. D. Y. Patil Arts, Commerce & Science College, Pune-18, Maharashtra, India
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1413SP030
Received: 26 June 2025; Accepted: 30 June 2025; Published: 24 October 2025
Abstract: Urban traffic is more stressed with the increase in traffic offenses like signal jumping, overspeeding, and helmet-less
driving, which are compromising the safety of the roads and making transportation inefficient. Traditional methods of enforcement
that rely on human monitoring and post-incident analysis are inadequate for real-time intervention. This paper introduces a future-
proof smart traffic violation detection system driven by Edge AI and Internet of Things (IoT) technology to provide efficient,
autonomous, and scalable traffic monitoring for contemporary urban environments. The envisioned system combines YOLOv5-
based object detection, optical character recognition (OCR) for license plate extraction, and OpenCV for visual analysis. Edge
devices like Raspberry Pi devices, along with IoT sensors, analyze and process video feeds at the point of origin, cutting latency
and bandwidth consumption drastically. Besides detection, the system uses machine learning-based predictive analytics to predict
hotspots of violations and peak hours, enabling authorities to implement preemptive safety measures. Real-time notification,
automatic reporting, and integration with smart city infrastructure further increase responsiveness and public accountability. Field
tests show high detection accuracy in various lighting and weather conditions, and the edge-IoT architecture provides cost savings
and simplified deployment. This research helps in developing intelligent transport systems, providing secure, intelligent, and
adaptable urban mobility solutions.
Keywords: Edge AI, IoT, Smart City, Traffic Violation Detection, YOLOv5, License Plate Recognition, Computer Vision, Real-
time Monitoring, Intelligent Transportation System, Predictive Analytics.
1. I. Introduction
The urban transport networks present are put under immense pressure by the speedy urbanization, population increase, and
exponentially growing vehicle ownership. With this expansion comes an increase in traffic violations of the type like jumping
signals, overspeeding, helmet-less riding, triple riding, and unauthorized parking most of which directly result in road crashes,
congestion, and compromised public security. Conventional methods of traffic enforcement based on manual observation and
penalty procedures following violations are frequently reactive, resource-based, and subject to human fallibility. These result in
poor enforcement and slow response in emergency situations. The development of smart cities demands clever, automated, and
scalable solutions for safer and more efficient mobility. In this scenario, Edge Artificial Intelligence (Edge AI) and Internet of
Things (IoT) integration has transformative possibilities. Edge AI allows for the processing of data in real-time at or near the
origin—like roadside cameras or edge devices—removing latency brought about by sending large amounts of video data to central
servers. With IoT infrastructure coupled with it, such systems are able to detect, report, and monitor traffic offenses independently
with little human involvement. This article presents a Next-Generation Smart Traffic Violation Detection System that utilizes
YOLOv5-based object detection, optical character recognition (OCR), and OpenCV-based image processing, all running on edge
devices such as Raspberry Pi or NVIDIA Jetson Nano. The system identifies multiple offenses, reads vehicle license plate numbers,
and employs a predictive machine learning layer to predict high-risk areas and offense-vulnerable time frames. Real-time alerts are
triggered and disseminated through SMS or dashboards for law enforcement. In contrast to traditional methods that center on post-
event audit, the new system accommodates proactive surveillance, real-time violation reporting, and anticipatory safety analytics.
Its cost-effective and modular design makes it adaptable for both high-density cities and underprivileged semi-urban regions. In
order to make urban mobility safer, smarter, and more responsive, the research aims to bridge the gap between intelligent
enforcement and reasonably priced smart infrastructure.
Motivation
The increased number of vehicles driving on city streets has resulted in a shocking increase in traffic offenses, leading to more
accidents, traffic congestion, and loss of human life. As per road safety statistics across the world, the major percentage of road
accidents is due to offenses like red-light jumping, overspeeding, and not wearing helmets. Manual regulation of traffic regulations
is more and more ineffective, particularly in urban areas, because there are limited personnel, man-made mistakes, and no ability
to observe more than one place at a time. Although traditional CCTV systems and intelligent surveillance provide partial remedies,
they tend to be based on central servers, which have high bandwidth expenses and introduce delay in processing. In addition, these
systems are not scalable, are not predictive, and do not have real-time enforcement capabilities. The inspiration for this study is to
create an affordable, real-time, and scalable solution that merges Edge AI and IoT to better the current systems. By operating data
at the edge, our system reduces latency and bandwidth needs, allowing real-time violation detection even in low-internet conditions.
By incorporating machine learning, there is predictive intelligence, enabling traffic departments to detect violation hotspots and
peak hours beforehand, hence facilitating proactive action. This research seeks to improve road safety, the efficiency of law
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 139
enforcement, and smart city infrastructure by providing a consolidated system for automatic, continuous, and intelligent traffic
monitoring with minimal intervention.
Objectives
The main aim of this study is to develop and deploy a smart, scalable intelligent system to detect and forecast traffic offenses
through Edge AI and IoT technology. The specific aims are:
To establish an in-real-time traffic offense detection system based on Edge AI and computer vision techniques like YOLOv5
to detect offenses such as red-light crossing, helmet-less riding, triple riding, and overspeeding.
To incorporate IoT devices like cameras, IR sensors, and edge devices for decentralized data collection and local processing
to minimize latency and reliance on cloud infrastructure.
To employ Optical Character Recognition (OCR) for automatic number plate recognition (ANPR) of offending vehicles to
facilitate traceability and automated penalty generation.
To create a predictive analytics model with machine learning to identify high-risk areas, peak hours of violation and probable
accident hotspots in order to aid proactive traffic control.
To implement an easy-to-use interface or dashboard for traffic officials to track violations in real-time, observe past trends,
and get automated alerts or violation reports.
To be cost-effective and scalable, so that the system can be implemented in urban and semi-urban areas without significant
infrastructural investment.
To test the performance of the system in different real-world conditions (lighting, weather, congestion) and assess accuracy,
speed, and reliability of detection and prediction modules.
Scope
The study aims to develop an intelligent and real-time traffic violation detection and prediction system that uses Edge AI and IoT
technologies to enhance urban road safety and traffic enforcement.
The system is intended to be scalable, affordable, and compatible with different urban and semi-urban settings. The system can
identify a variety of traffic offenses in real-time, such as red-light running, overspeeding, helmet-less riding, triple riding, and illegal
lane changing. Computer vision models, in this case YOLOv5, identify these offenses from live video from roadside cameras.
License plate recognition is facilitated through Optical Character Recognition (OCR), enabling offending vehicles to be recorded
and traceability facilitated for enforcement.
IoT devices like infrared sensors, cameras, and edge devices like Raspberry Pi or Jetson Nano are utilized to process and gather
data locally. This edge computing system reduces data transmission delays and the need for constant internet connectivity.
Machine learning modules in the system process past traffic data to forecast violation hotspots, risky time slots, and behavioral
patterns. This predictive feature enables proactive traffic management.
Real-time alerts and violation reports are produced and available for viewing via a dashboard. The interface enables traffic
authorities to track ongoing violations, view analytics, and browse logs with ease.
The system is engineered to be modular and deployable in either resource-intensive smart cities as well as infrastructure-light
regions, thus ensuring affordability and flexibility.
Nonetheless, some scopes are out of the range of this research. These are direct penalty enforcement handling (e.g., generation of
an e-challan), operation during severe weather conditions such as heavy fog, and the application of privacy-sensitive technologies
like face recognition. These points are mentioned for future work.
Understanding Traffic Violation Detection
Traffic violation detection is the process of automatically detecting and logging cases in which drivers or vehicles are violating
road traffic regulations. Violations cover offenses like red-light jumping, overspeeding, helmet-less driving, unauthorized U-turns,
triple riding, and parking without authority. Efficient violation detection is important for curbing road accidents, enhancing public
safety, and facilitating traffic movement in urban cities.
Historically, traffic violation enforcement was based on human observation—officers observing intersections manually or
monitoring CCTV images. Such a procedure is labor-intensive, time-consuming, and prone to human error. With increasing urban
density, manual process alone is incapable of providing efficient and effective enforcement.
Contemporary traffic violation detection uses a fusion of computer vision, artificial intelligence (AI), machine learning, and Internet
of Things (IoT)-based sensors for real-time, automated surveillance. Traffic camera video streams are processed by object detection
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 140
algorithms (e.g., YOLOv5) to detect vehicles, riders, and their behavior. Optical Character Recognition (OCR) is utilized to scan
vehicle license plates for identifying violators.
With Edge AI, these operations are performed on local devices (e.g., Raspberry Pi or Jetson Nano) at the camera location,
minimizing the requirement to send large amounts of information to central servers. This facilitates quicker decision-making and
response, even in regions with poor internet connectivity.
Moreover, by integrating machine learning algorithms, these systems are capable of extending beyond detection. They can learn
from past histories, forecast areas likely to violate, and recommend preventive measures. For instance, the system can detect that
red-light jumping is most common during late evening hours at particular intersections and warn traffic authorities accordingly.
The major elements involved in traffic violation detection are:
Cameras for round-the-clock video surveillance.
IR sensors to identify vehicle crossing during red lights.
Edge devices for local processing and analysis.
Models of computer vision for detecting objects and behavior.
Databases and dashboards for logging infractions and facilitating visualization.
Traffic violation detection is critical to the implementation of smarter urban mobility systems. It transitions enforcement from
reactive to proactive, making cities safer, more efficient, and better able to handle increasingly heavy traffic loads.
Data Science in Traffic Violation Detection
Data science is essential to improving the speed, accuracy, and intelligence of traffic violation detection. Through the analysis of
huge amounts of traffic data—video content, vehicle speeds, license plate numbers, and time-stamped violation records—data
science makes systems capable of spotting patterns, anticipating violations, and facilitating data-driven decision-making.
Machine learning algorithms, particularly computer vision-based ones such as YOLOv5, are taught to identify vehicles, identify
offences like jumping red lights or riding without helmets, and categorize vehicle classes. Optical Character Recognition (OCR) is
applied to read license plate numbers from images and associate them with vehicle owner databases.
Using historical information, predictive analytics can detect high-risk areas and rush hours for infringements so traffic authorities
can act proactively. Preprocessing methods such as image scaling, denoising, and feature extraction enhance detection accuracy
and minimize false positives.
Data science in general converts raw traffic videos into actionable information, which contributes to constructing intelligent, real-
time systems for more secure and intelligent urban mobility.
Figure 1: Plot of model accuracy and validation accuracy versus training epochs for the violation detection model.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
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Analysis of Monthly Traffic Violation Trends
The bar graph depicts traffic violations from January to June, divided into overspeeding, red-light jumping, helmet-less riding, and
triple riding. Overspeeding reveals the maximum and most erratic figures, reaching a peak in May. Red-light jumping increases
consistently, reflecting habitual non-compliance. Helmet-less riding and triple riding depict increasing trends, indicating an
emerging requirement for focused enforcement. The evidence points towards May and June as risky months, justifying real-time
monitoring and predictive traffic management.
II. Methodology
The research approach to this study entails the creation of real-time intelligent traffic offense detection and forecasting system that
utilizes Edge Artificial Intelligence (AI), Internet of Things (IoT) elements, and computer vision methods. The system is capable
of independent tracking and monitoring of traffic behavior, detection of offenses, identification of car license plates, and timely
notification while also employing machine learning to identify possible high-risk areas and peak violative hours. The following
describes each part of the system architecture and the step-by-step processes of reaching the overall functionality.
Data Acquisition -The system starts from the real-time traffic data collection by mounting high-resolution IoT-based cameras at
places prone to violations. The cameras record under different lighting and weather conditions. Infrared (IR) sensors capture
incidents such as stop-line violations and no-parking zone entrance. Local processing is done by edge devices like Raspberry Pi or
Jetson Nano. Data is tagged with metadata (camera ID, location, signal status) and timestamped for further analysis.
Data Preprocessing -Preprocessing is done for captured video data by taking frames at fixed intervals in order to balance speed
and accuracy. Frames are resized (normally to 416×416 pixels), normalized, and filtered in order to eliminate noise such as shadows
and glare. Areas of interest (ROI), e.g., stop lines or parking areas, are separated to enhance detection precision and eliminate false
positives in traffic analysis.
Object Detection using YOLOv5 Following preprocessing, YOLOv5 is employed to identify objects such as vehicles, helmets,
and license plates in every video frame. The model, trained on a custom traffic dataset, detects violations like signal jumping,
helmet-less riding, and triple riding through bounding boxes and confidence scores. Violation classification is verified against pre-
defined rules for detected actions.
Estimation of Speed and Lane Violation The system monitors car movement between frames with centroid tracking and optical
flow to estimate speed. When a car has gone over the local limit, it is detected. Lane offenses are detected by matching car positions
with lane lines using perspective transformation methods.
License Plate Recognition via OCR When a violation is detected, YOLOv5 identifies the license plate area. The area is scanned
by Tesseract OCR to recognize alphanumeric characters, which is then compared to vehicle registration information for
identification and notification.
Violation Logging and Alerting All the violations are recorded in a structured database with accompanying details and image
evidence. There are real-time alerts to violators and traffic authorities through SMS or email, and every case is assigned a unique
violation ID for tracking and enforcement.
Predictive Analytics Using Machine Learning Historical information is modeled with the aid of algorithms such as Random Forest
and Gradient Boosting to forecast high-risk areas and times of future violations. The outcomes are visualized in heatmaps and
graphs to assist authorities in proactive enforcement planning.
Visualization and Reporting Interface A Flask-based dashboard presents live data, trends, and analytics. Authorities can filter
and export reports, track violations by category, and visualize predictive insights to enhance traffic planning.
Implementation Tools and Environment, It is implemented using Python, OpenCV, PyTorch (YOLOv5), Tesseract OCR,
Pandas, and NumPy. It has Docker-based deployment and stores data in SQLite or Firebase. GitHub Actions maintains automated
testing and version control.
III. Literature Survey
Traffic violation detection challenge has been pursued in several fields, most prominently with the integration of image processing,
IoT, and artificial intelligence. Several research works have submitted models enhancing enforcement efficiency and public safety
through smart technologies.
Manogna et al. (2022) proposed a camera-based traffic signal violation system using IoT and image processing. Their method
captures license plates and sends violation reports to authorities. While effective, the system relies heavily on post-event analysis
and lacks predictive capabilities.
Raj Anand et al. (2021) presented a deep learning system with YOLOv3 for speed detection and signal jumping. Their platform
detected vehicle count exceeding 97% and speed violation detection at 89%. It did, however, demand high-end hardware and central
servers to process, which makes it less practical for real-time edge-based deployment.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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www.ijltemas.in Page 142
Srikanth et al. (2025) implemented a broad violation detection platform based on YOLOv5, OCR, and IoT sensors. The system
is capable of detecting various violations including helmet-less riding, triple riding, and unauthorized parking, and is integrated
with SMS alert systems. Their application of open-source tools such as TensorFlow and OpenCV kept the model cost-effective and
scalable, though the performance of the model under low-lighting was still to be improved.
Mohammed et al. (2023) propose an over-speeding detection system that integrated GPS and driver images with computer vision
and machine learning. Their focus was on real-time detection and system adaptability under changing lighting and traffic conditions.
Nonetheless, their contribution was focused only on speed-related offenses.
In general, current research emphasizes the increasing capability of AI and IoT for intelligent traffic monitoring. Nevertheless, most
systems lack real-time edge processing, multi-violation detection, or predictive analytics. This work intends to fill the gaps by
integrating Edge AI, IoT, and machine learning into one real-time, scalable, and unified platform.
IV. Conclusion
This paper introduces a holistic and scalable smart traffic violation detection and prediction scheme via Edge AI and IoT
technologies. The system, through the integration of real-time video processing, YOLOv5 object detection, optical character
recognition (OCR), and predictive machine learning models, automates the detection of severe traffic violations like red-light
jumping, speeding, riding without a helmet, triple riding, and failing to park.
The application of edge computing platforms such as Raspberry Pi and Jetson Nano supports on-site, low-latency processing, which
makes the system very efficient and deployable in both smart cities and areas with limited infrastructure. In addition, the predictive
analytics module provides an advanced layer of proactive traffic enforcement by determining high-risk sites and high-viation time
periods.
The visualization dashboard facilitates ease of use for traffic authorities by providing actionable insights, violation patterns, and
evidence-based reporting. This research has immense potential for minimizing manual enforcement efforts, enhancing road safety,
and advancing the development of intelligent transportation systems.
References
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