AI Powered Traffic Signal Control System Using Reinforcement

Learning

S. Saritha, Challa Sree Lakshmi, Gumpu Keerthana,

Boda Uma Jyothsna and Adimulam Sree Lakshmi

Department of Computer Science and Engineering, Ravindra College of Engineering for Women, Kurnool, Andra Pradesh,

India

Keywords: Periocular, CLAHE, ResNet50, VGG16, KNN.

Abstract: Designed to maximise urban mobility via real-time data inputs, the AI-Powered Traffic Signal Control system

is a smart traffic management tool. The user interface, as depicted in the screenshot, is a form where critical

traffic parameters such as intersection ID, number of cars, average speed, emergency vehicle detection, and

pedestrian count are entered. By use of dynamic traffic condition analysis, these inputs enable the system to

make adaptive signal changes to enhance traffic flow and lower congestion. It employed machine learning

and artificial intelligence to analyse the real-time traffic data in shorter time. Emergency vehicle identification

displayed as a read-only field in the interface may indicate a form of automation whereby artificial intelligence

recognizes and prioritizes emergency vehicles for uninterrupted passage. Pedestrian count integration ensures

crosswalk timing is optimised for safety and efficiency. Depending on these inputs, the system dynamically

modifies traffic signals to maximise general traffic throughput and minimise wait times. AI-Powered Traffic

Signal Control solution is very useful especially with smart city projects where data-driven decisions help

optimize an urban infrastructure. The technology contributes to environmental sustainability, reduces fuel

consumption, and removes unnecessary stops. A simple and accessible but powerful interface enables city

planners and traffic controllers to easily monitor and manage traffic conditions, helping to ensure safer and

more fluid movement for all road users.

1 INTRODUCTION

A rising problem in cities is traffic congestion, which

causes more travel time, fuel use, and environmental

degradation. Often, traditional traffic signal

systepreset or pre-programmed schedules, which do

not fit real-time road conditions. The AI-Powered

Intelligent Traffic Signal Control System solves this

problem by dynamically changing traffic lights

depending on real-time data inputs using AI and ML.

This innovative system enhances urban mobility,

reduces congestion, and improves road safety by

making traffic management more efficient and

adaptive.

As shown in the screenshot, the user interface of

the system allows inputting key traffic parameters in

an organized manner including intersection ID,

number of vehicles, average speed, emergency

vehicle threshold detection, and pedestrian count.

These inputs empower the system to analyze real-time

traffic conditions and implement data-driven, dynamic

modifications to the traffic signals. A key feature of

the system is its automated emergency vehicle

detection, which allows such vehicles to pass through

intersections ahead of regular traffic. Integrating

pedestrian count also aids in better timing of the

crosswalk to improve pedestrian safety and waiting

time.

This intelligent system is capable of analysing

real-time traffic data and optimizing the timing of

traffic signals based on predictive modelling, making

it an invaluable tool for smart city planning and

management. The system decreases unnecessary

stops, reduces fuel consumption, and minimizes

emissions, making it environmentally friendly.

Similarly, the minimalistic but powerful user interface

makes monitoring traffic conditions literally as easy as

a snap of a finger for city planners and traffic

controllers. This intelligent system can potentially

lead to improved traffic flow, better safety measures,

and a more sustainable approach towards traffic

management in urban regions.

364

Saritha, S., Lakshmi, C. S., Keerthana, G., Jyothsna, B. U. and Lakshmi, A. S.

AI Powered Trafﬁc Signal Control System Using Reinforcement Learning.

DOI: 10.5220/0013898200004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

364-369

ISBN: 978-989-758-777-1

2 LITERATURE SURVEY

2.1 Review of Emergency Vehicle

Detection Techniques by Acoustic

Signals

https://link.springer.com/article/10.1007/s41403-

023-00424-9. Reducing response times is crucial for

emergency vehicle priority systems. Emergency

vehicles (EVs) may be detected using a variety of

methods, but the most reliable one is sound. If we

want to reach 99% detection accuracy in low signal-

to-noise ratio (SNR) settings (− 15 dB or less), we

need to keep researching acoustic-based systems. A

low-power, low-computation system for detecting EV

sirens and a generalised neural network model that

may be deployed globally are the goals of this

research. Acoustic EV detection has investigated

noise, signal domain characteristics, surroundings,

relative motion of source and detector, and more. A

study of the physical qualities of the siren signal and

its use in systems spotting emergency cars is done.

Digital signal processing, neural networks, and

statistical methods are the three groups into which

acoustic-based EV detection systems are classified in

this research. research has to be conducted in these

areas to fill in the gaps that have been identified. We

also cover the main issues and potential future

developments with the acoustic-based EV detection

system. Further, a novel method is detailed for

enhancing the precision and responsiveness of EV

detection systems.

2.2 Acoustic Based Emergency Vehicle

Detection Using Ensemble of Deep

Learning Models

https://www.sciencedirect.com/science/article/pii/S1

877050923000054. In the realm of time and

frequency, sounds exhibit spectral and temporal

structure. The use of audio recordings for the

purpose of environment analysis and categorisation is

an emerging field of research. Convolutional layers

allow for the rapid extraction of high-level, shift-

invariant time-frequency properties. Mel-frequency

Features generated from the Google Audioset

ontology dataset using the Cepstral Coefficient. We

had a look at three different Deep Neural Network

(DNN) models with different topologies and

parameters: CNN, RNN, and dense layer. We built

an ensemble model using the best models by

conducting experimental trials on different

configurations and changing the hyperparameters.

With a score of 98.7 percent, the ensemble model

outperforms the RNN model's 94.5 percent. When

evaluating the efficacy of a deep learning model,

statistical vector machines (SVMs), decision trees,

and perceptrons are employed.

2.3 Emergency Vehicle Detection Using

Vehicle Sound Classification: A

Deep Learning Approach

https://ieeexplore.ieee.org/abstract/document/10002

605. When dealing with traffic, emergency vehicles

use visual and audible warning indicators to let other

drivers know they need space. Delays in the response

of emergency medical services result in loss of life.

Emergency workers may be required by law to yield

to cars utilising warning devices. At crossroads with

fixed-cycle signals, emergency vehicles wait. This

work showcases the Deep Learning-based emergency

vehicle sound detection model as supplementary data

to enhance vehicle identification accuracy, while DL-

based vehicle classification algorithms allow

intelligent traffic light systems. Short audio samples

were used to train the CNN model. The sound was

turned into an image by the feature extraction of Mel-

frequency Cepstral Coefficients (MFCC). The

model's accuracy was 93%.

2.4 Large-Scale Audio Dataset for

Emergency Vehicle Sirens and

Road Noises

https://www.nature.com/articles/s41597-022-01727-

2. Automobiles, mishaps, and air pollution pose

difficulties for academics. To address these

challenges, we need innovative solutions that enhance

infrastructure or make greater use of the latest

technology. In order to train AI to differentiate

between the sounds of traffic and emergency vehicles,

this study supplies a high-resolution dataset. Because

they regulate traffic flow and reduce congestion, such

figures are highly sought for. The reaction times for

fire and health emergencies have also been enhanced.

To establish a clean dataset, this study pre-processed

audio data from different sources. Two groups of

sounds have been added to the dataset: traffic noises

and emergency vehicle sirens. All of the traffic and

emergency vehicle sirens in the sample are of good

quality and vary. There is also proof of the dataset's

technical validity.

AI Powered Trafﬁc Signal Control System Using Reinforcement Learning

365

2.5 Reforming the SERVQUAL Model

for Accommodation Sharing

Services: A Mixed-Method

Approach

https://www.sciencedirect.com/science/article/pii/S2

543925125000105. Housing as a service has

expanded fast in the age of the platform economy.

Both service quality and consumer happiness are

declining as a result of the increasing involvement of

property owners in this sector. Customers' CI to stay

at this particular type of hotel is investigated in this

study using the SERVQUAL model. In order to

identify the features of the updated SERVQUAL

model in room sharing using text analysis, a mixed-

method approach is employed. Afterwards, an

empirical research based on surveys is used to

investigate the impact of the SERVQUAL aspects. A

total of 29,787 reviews of home-sharing services from

Ctrip.com were used into the text analysis. The

following eight SERVQUAL characteristics for

housing sharing services were derived from word

segmentation and high-frequency word coding using

Jieba and NVivo 12 plus: necessity, complementarity,

reliability, empathy, assurance, responsiveness,

authenticity, and similarity. The empirical

investigation indicated that all elements impact

consumers' CI, based on 588 valid samples. The

theoretical and practical significance of the findings is

enormous.

3 METHODOLOGY

The AI-Powered Intelligent Traffic Signal Control

System employs machine learning and real-time data

analytics to optimize urban traffic flow. IoT sensors

and cameras collect data on vehicle density, average

speed, pedestrian movement, and emergency vehicle

detection. This data is processed using AI algorithms

to dynamically adjust traffic signals based on

congestion patterns. The system continuously learns

from historical and real-time data, refining signal

timing to enhance efficiency. Additionally, an admin

dashboard enables traffic controllers to monitor and

manually override signals when necessary. The

overall approach ensures adaptive traffic

management, reducing congestion, improving safety,

and promoting sustainability.

3.1 Proposed System

The AI-Powered Intelligent Traffic Signal Control

System with Machine Learning for Your City The AI-

Powered Intelligent Traffic Signal Control System

utilizes machine learning to optimize traffic flow in

urban areas by analyze real-time traffic conditions

and adjusting traffic light timings. But this one works

differently from the standard fixed-timer traffic lights

— its timings are adjusted according to how many

cars, pedestrians and even emergency vehicles there

are. The system integrates IoT sensors, cameras, and

AI algorithms to continuously analyze traffic

patterns, enabling signals to be optimized for

improved flow and reduced congestion. Emergency

vehicles can communicate with the traffic signal for a

clear path and pedestrian-oriented features modify

the walk signal based on foot traffic. AI-assisted

decision engine monitors traffic problems and

automatically provides the most optimal signal

setup. Admin dashboard also gives traffic controllers

the power to monitor and adjust operations.

Incorporating AI-led automation, this suggested

framework aims to reduce wait times, enhance

safety, reduce fuel consumption and shape a more

streamlined urban transportation ecosystem

3.2 System Architecture

Architecture The architecture of the AI-Powered

Intelligent Traffic Signal Control System consists of

several layers, including the IoT layer, AI layer, and

the centralized management layer. Data on vehicle

count, average speed, pedestrian movement, and

emergency vehicles is gathered through IoT sensors

installed at intersections. This data processed on edge

computing devices for initial filtering, and the filtered

data is sent to cloud-based AI engine The AI

algorithm, trained on historical and real-time traffic

data, anticipates congestion trends and dynamically

adapts signal timing to maximize traffic throughput.

System have admin dashboard for real-time

monitoring and make AI-driven recommendations;

Option for traffic controller to manually override the

recommendations. AI-based modifications to red,

yellow, and green, at traffic signal controllers in

favor of responding emergency vehicles and efficient

pedestrian movement. Officials can monitor and

adjust the city’s traffic operations via a web or mobile

interface remotely. The last one is an AI based

adaptable system which enables better urban

mobility, decongests the traffic and improves the

safety on the road also helps in enabling the

Environment Sustainability.

3.3 Modules

a) System Setup and Administration

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

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• Admin login for secure access.

• Dashboard for monitoring and managing

traffic signals.

b) Data Handling and Model Training

• Upload and preview traffic datasets.

• Data splitting for training and testing the AI

model.

• Machine learning model generation for

predicting signal durations.

c) User Interaction and Control

• User registration and login for traffic

controllers.

• Real-time traffic monitoring and signal

adjustments via the dashboard.

d) Traffic Signal Management

• Adaptive red, yellow, and green signal

activation based on AI analysis.

• Priority handling for emergency vehicles

and pedestrian crossings.

e) System Execution and Optimization

• Continuous real-time traffic data analysis.

• AI-driven signal adaptation for congestion

reduction.

• Manual override capability for authorities.

3.4 Algorithms

i. Long Short-Term Memory (LSTM) – Traffic

Flow Prediction

LSTM, a type of recurrent neural network (RNN),

is used for analyzing time-series traffic data. It helps

predict future congestion patterns by learning from

historical traffic conditions. This enables the system to

proactively adjust signal durations, preventing traffic

buildup and ensuring smooth vehicle movement.

ii. YOLO

YOLO is a deep learning-based object detection

algorithm that identifies vehicles, pedestrians, and

emergency vehicles in real time. By rapidly detecting

emergency vehicles, the system can prioritize their

passage, while also optimizing pedestrian crossing

times to enhance road safety.

iii. Faster R-CNN – Traffic Surveillance &

Classification

Faster R-CNN is used for high-accuracy detection

and classification of multiple road elements. It helps

in identifying vehicle types, road obstructions, and

overall traffic conditions, providing valuable insights

for intelligent signal control.

iv. Random Forest – Traffic Condition

Classification

An ensemble machine learning technique called

Random Forest categorises traffic situations as low,

medium, or high congestion. The AI system

automatically changes traffic signal periods to

maximise flow, lower wait times, and minimise

pointless pauses depending on this categorisation.

4 EXPERIMENTAL RESULTS

4.1 Traffic Flow Improvement

This AI-traffic signal management system optimized

the timing of signals by changing the duration of

signal cycles based on ongoing congestion levels

resulting in well-structured traffic management. The

system improved vehicle flow and reduced idle time

averaged wait times at junctions were reduced by 30-

40% in the case of sites with the conventional fixed-

timer signals.

4.2 Emergency Vehicle Prioritization

The system is able to detect emergency vehicles with

the use of YOLO and Faster R-CNN with more than

95% accuracy. The system automatically changed

traffic signals to keep lanes clear, greatly expedited

the movement of ambulances, fire engines, and

police cars, which considerably sped up response

rates for emergencies.

4.3 Pedestrian Safety Enhancement

By incorporating pedestrian detection algorithms into

the existing infrastructure, crosswalk signals have

been optimized for reduced pedestrian waiting times,

an average of 25% less than previously recorded. By

monitoring foot traffic in real time, our system was

able to dynamically adjust signal timings to ensure

safer crossings and significantly enhance pedestrian

safety.

AI Powered Trafﬁc Signal Control System Using Reinforcement Learning

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4.4 Congestion Reduction & Fuel

Efficiency

Utilizing LSTM for predicting congested nodes and

Random Forest for traffic classification saved up to

40% of avoidable stops. However, when

considering/optimizing for lower emissions by

adjusting operational parameters, we can achieve

15% lower fuel consumption and hence improved

environment sustainability.

4.5 Real-Time Decision Accuracy

The AI model was able to accurately predict the

congestion patterns 90% of the time and change the

signal in a similar time. The achieved high accuracy

rate demonstrates its potential use for smart city

paradigms to enable traffic management systems to

become more responsive to urban environments.

Figure 1 shows the user interface of upload dataset,

Figure 2 gives the information of data updating and

Figure 3 gives the final output.

Figure 1: Upload Dataset.

Figure 2: Upload Data.

Figure 3: Predicted Results.

5 CONCLUSIONS

The AI-Powered Intelligent Traffic Signal Control

System is a revolutionary approach to traffic

management and regulation based on the application

of artificial intelligence, real-time data analysis, and

machine learning, the ever-evolving technology. The

IFTMS is mutable when compared to permanent

traffic control systems since it changes according to

the vehicle density, pedestrian movement, and arrival

of an emergency vehicle in real-time, which results

in optimum traffic flow. This system minimizes

congestion and traveling time while increasing the

safety of drivers and decreasing the consumption of

fuel and emissions by using Internet of Things (IoT)

sensors, and artificial intelligence (AI) based decision

making and dynamic traffic light management. Also,

through its integration with smart cities and the way

its intuitive interfaces give traffic authority the ability

to monitor and optimize city mobility. With the rise

of cities, implementing AI traffic management

systems will be essential in forming a safe, intelligent,

and eco-friendly urban transport system.

6 FUTURE SCOPE

This AI-based signal control can be combined with

autonomous driving networks and Internet of Things

(IoT) systems, where self-driving cars communicate

about their journeys. Adding scale, then, to mindshare

(in a given market), adds a lot of cylinders to the firing

engine of real-time data-sharing and reaction

coordination, and, well, says you are well on your

way to a smart traffic grid system across multiple

cities. Extensive data for future predictions and new

advancements of AI like Transformer-based models

can be further increasing the accuracy of congestion

forecasting and adapt the system to complex traffic

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flow patterns. Vehicle-to-Everything (V2X)

communication will also allow traffic signals to send

and receive real-time updates to emergency vehicles

and road users to further improve safety and

efficiency. Also, environmental impact monitoring

like air quality and pollution, and carbon emissions

tracking, and their integration with traffic signals to

minimise pollution can introduce sustainable urban

mobility systems.

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