ADAPTIVE TRAFFIC SIGNAL MANAGEMENT

Abstract

ABSTRACT OF THE INVENTION This invention introduces an advanced traffic management system using AI and deep learning to tackle urban traffic congestion by optimizing signal timings in real time (1 00,103,1 04). Traditional traffic signals operate on a fixed schedule, causing inefficiencies during peak hours. The new system uses YOLO v8 (1 02,1 05), a highaccuracy object detection algorithm, to process live video feeds from traffic cameras (1 01 ). It counts vehicles and estimates traffic density to adjust signal timings dynamically. A Deep Q-Learning Network (DQN) is employed to predict and adjust signal phases, continuously learning from traffic patterns and reduced in vehicles and queue lengths

Specification

DESCRIPTION:
Field of Invention:
[0001] This invention relates the field of traffic management and artificial
5 intelligence, it uses deep learning techniques to detect the traffic volume in each
direction and provide a solution for congestion in traffic junctions.
Complete Specification:
I 0 [0002] This application is an advanced system designed to manage traffic more
efficiently by modifying signal countdown time based on volume of real time
traffic in each direction. Unlike traditional traffic signals, which follow a fixed
schedule, this invention dynamically changes their timings with respond to current
traffic demands.
15 [0003] This system uses a deep learning algorithm called as YOLO v8, it can
detect vehicles, pedestrians, cycles and more, making it an ideal solution for
adaptive traffic control systems. Performance Monitoring is essential to ensure
the system is meeting its goals. YOLO AI continuously monitor traffic in real time,
collecting data on vehicle counts, speed and congestion levels. We use
20 established metrics and KPis to assess system performance adjusting signal
controls as needed to optimize traffic flow.
[0004] Machine Learning model can predict optimal traffic signal phases based
on current traffic conditions. A reinforcement learning algorithm can then
determine the best action to take, ensuring that the outcome leads to improve
25 traffic flow. By implementing an advanced Deep Q-Learning Network instead of
the traditional Round-Robin method, the system can effectively minimize overall
vehicle delay and reduce traffic congestion.
[0005] Traffic light coordination is achieved by integrating low-cost
mlcrocontroller chips, to control signal transitions. These microcontrollers are
30 programmed to manage the operation of traffic lights in a customized manner,
adapting the timing based on the detection of objects and ensuring efficient traffic
flow. Once the objects are detected, the system will show the direction which has
the highest and lowest vehicle flow. According to the output given by the system,
is given more signal timings.
[0006] Furthermore, according to the situation and specified cases such as
emergency vehicles, constructions this invention can be improvised by using
5 sophisticated algorithms to monitor traffic patterns efficiently.
PRIOR ART AND BACKGROUND:
10 [0007] Traffic jams are one of the common problems we all face, the major root cause
for traffic occurs from junctions and intersections. Usually traffic signals have constant
timings, this results in congestion in heavily traffic junctions, by introducing varying
signaltilllings we are able to customize the timings according to the vehicle flow in each
direction.
15
[0008] Several patents have been filed regarding adaptive traffic signal systems,
focusing on improving traffic management through various dynamic adjustments to the
traditional traffic signal:
· 20 1. US2020365015A1: This describes a connected and adaptive traffic management
system using software like SCOOT and In Sync 12. The system collects real-time data
from traffic cameras, sensors, and connected vehicles. Adaptive traffic signals are
adjusted dynamically based on this data to optimize flow and reduce congestion .
Algorithms prioritize certain traffic types, such as emergency vehicles and public
25 transport. Integration with smart city infrastructure enhances overall traffic efficiency.
2. US20220335340A1: Describes a system using software like Edge Environment
Orchestration and machine· learning models. The solution monitors data usage in
real-time to identify and mitigate ethical divergences, generating alerts when potential
30 issues are detected. This ensures ethical data usage through continuous monitoring
and proactive mitigation.
N 3.CN112750326B: This describes a generalized vehicle-road cooperation method using
software like traffic information acquisition modules and roadside intelligent

control and serv1ce information based on vehicle positions. It aims to ensure traffic
safety, improve travel efficiency, and provide accurate services by integrating and
coordinating data from vehicles, roads, and intelligent facilities.
5 4.CN217061186U: describes traffic incident monitoring equipment using software like
camera management modules and data format conversion modules. The solution
combines intelligent high-definition cameras and millimeter wave radar to collect road
information, improving precision. It synchronizes data from cameras and radar to
eliminate errors caused by time discrepancies, enhancing the accuracy of traffic
1 o incident.
5. CN1 042690518: describes an expressway monitoring and management
system using software like automatic traffic accident detection systems and
video monitoring systems. The solution involves a monitoring center that collects
1 5 and processes traffic data in real-time, issuing control and guidance information
to enhance traffic flow, reduce congestion, and improve safety. This system aims
to increase travel efficiency, reduce accidents, and provide economic, social, and
environmental benefits.
20 6. CN111274970B: describes a traffic sign detection algorithm using an improved
YOLO v3 software. The solution enhances detection precision for small traffic
signs by replacing DarkNet-53 with a high-resolution feature extraction network,
fusing feature maps, and· optimizing the loss function with GloUand focal loss
algorithms. This approach allows for quick and accurate detection and
25 identification of traffic signs on complex roads.
OBJECTIVE:
[0009] Traffic jam is a significant problem in urban areas. This leads to additional
30 travel time, fuel usage, and pollution. The existing traditional traffic signal systems
operate on fixed schedule and do not adapt to real-time traffic conditions. An
adaptive traffic signal system using YOLO v8 aims to address these
challenges by dynamically adjusting wait time at signals based on traffic data.
This system leverages advanced object detection algorithms to improve traffic
Real-Time Analysis:
[001 0] The core of the adaptive traffic signal system is its ability to perform realtime
analysis of traffic conditions. YOLO v8, a deep-learning based-object
5 detection algorithm, processes live feeds from CCTV cameras installed at
intersections. This algorithm is capable of detecting and classifying various
·objects, including vehicles, pedestrians, and cyclists, with high accuracy and
speed. By continuously monitoring the traffic flow, the system gathers essential
data on the number and types of vehicles approaching the intersection.
10
Density Estimation:
[0011] Once the objects are detected and classified, the system calculates the
traffic density at each intersection. Traffic density estimation involves counting the
count of vehicles in individual lanes help in determining the overall congestion
15 level. This information is crucial for making informed decisions about signal
timings. For instance, if the system detects a high density of vehicles in one
direction, it prioritizes the direction with more vehicles by giving green signals for
that direction to reduce congestion.
20 Signal Adjustment:
[0012] Based on the real-time traffic analysis and density estimation, the adaptive
traffic signal system dynamically adjusts the signal timings. This process involves
extending or shortening the duration of green, yellow, and red lights to optimize
traffic flow. For example, during peak hours, the system can allocate more green
25 time to heavily congested lanes, to minimize unnecessary delays. The goal is to
ensure a smooth and efficient flow of traffic, reducing wait times and preventing
bottlenecks.
Monitoring Interface:
30 [0013] To facilitate real-time monitoring and control, the system includes a userfriendly
interface for traffic management authorities. This interface provides a
camera feeds, traffic density data, and signal timings. Traffic managers can use
this interface to manually override the system if necessary, respond to
emergencies, and make data-driven decisions to improve traffic management
strategies.
SUMMARY:
[0014] This invention relates to traffic management using artificial intelligence,
specifically deep learning techniques, to detect traffic volume in different
10 dire.ctions and optimize traffic flow at junctions. It uses the YOLO vB algorithm for
real-time detection of vehicles, pedestrians, and cyclists. The system dynamically
adjusts wait time at signals based on the number of waiting vehicles, unlike old
fixed-schedule signals.
15
[0015] The machine learning model predicts traffic signal phases, while a Deep
Q-Learning Network (DQN) optimizes traffic flow, reducing vehicle delay and
queue length. The system uses micro controller chips for low-cost, coordinated
traffic light control, adjusting signals based on real-time traffic patterns.
By accurately detecting and classifying objects, the system can reduce the risk of
accidents at intersections. It ensures that pedestrians and cyclists are given
20 adequate time to cross the road safely. Dynamic signal adjustments based on
traffic conditions help minimize delays and reduce overall travel time for
commuters.
[0016] The system optimizes traffic flow, preventing congestion and bottlenecks,
which leads comparatively efficient traffic movement and idle time. This system
25 helps lower fuel consumption and emissions, contributing to a cleaner
environment. It helps traffic control department to take decisions at real time
without. manual intervention.
DETAILED TECHNICAL DESCRIPTION:
30
[0017] Technical Description for Adaptive Traffic Signal System Using YOLO v8
Overview
adaptively adjusting wait time at signals based on real-time data. By leveraging
YOLO· vB's advanced object detection capabilities, the system monitors and
analyzes traffic conditions at intersections, ensuring efficient management and
reduced congestion.
5 Components
[0019)
1. CCTV Cameras: Strategically placed cameras capture live video feeds from
traffic intersections.
2. YOLO v8 Model: Processes the video feeds to detect and classify vehicles and
10 pedestrians in real-time.
3. Traffic Density Estimation Module: Calculates the density. of traffic based on
the detected objects.
4. Signal Control Module: Adjusts traffic signal timings dynamically·based on the
traffic density.
15 5. Monitoring Interface: Provides real-time data and control options for traffic
Q) management authorities.
[0020) CCTV cameras continuously capture live video feeds from intersections.
20 The YOLO v8 model processes these video feeds to detect the various objects,
such as cars, buses, motorcycles, and pedestrians. The detected objects are
used to calculate the traffic density at each intersection. This involves counting
the count of vehicles in each lane and determining the overall congestion level.
Based on the traffic density, the signal control module dynamically adjusts the.
25 timings of traffic lights. This includes extending or shortening the duration of
green, yellow, and red lights to optimize traffic flow. The monitoring interface
displays real-time traffic data, including live camera feeds, traffic density, and
current signal timings. Traffic management authorities can use this interface to
manually override the system if necessary and respond to emergencies.
[0021] Figure 1
5 · 1. CCTV Cameras:
Input: Installed at intersections to capture real-time video feeds of traffic conditions.
Function: These cameras provide continuous footage that serves as input for traffic
analysis.
2. YOLO v8 Object Detection Module:
10 Input: Real-time video feed from CCTV cameras.
Function: YOLO v8 processes the footage to detect and classify objects like vehicles,
pedestrians, and cyclists. This detection is rapid and accurate, ensuring real-time
<:~nalysis. __ _
3. Traffic Density Estimation Module:
1.5 Input: Detection data from YOLO v8 (count and classification of vehicles).
Function: This module calculates traffic density by counting vehicles in each lane and
identifying congestion levels. It is critical for determining signal timings.
4. Signal Control Algorithm:
Input: Traffic density data from the estimation module.
20 Function: Based on the density,·this algorithm dynamically adjusts the duration of green,
yellow, and red signals to optimize traffic flow, reduce wait . times, and manage
congestion effectively.
5. Monitoring & Control Interface:
lnpuUoutput: Displays real-time traffic data (live feeds, density, signal status) for traffic
25 authorities.
Function: Provides a user-friendly interface for manual overrides, emergency
responses, and data-driven decisions. Authorities can intervene if necessary .
6. Centralized Database & Analytic:
Input: Real-time traffic data from multiple intersections.
30 Function: Stores historical traffic patterns, enabling long-term data analysis to improve
future traffic strategies and system optimizations.
N [0022] CCTV Cameras capture real-time traffic feeds. YOLO .v8 detects vehicles and
~ pedestrians from the footage with high accuracy. The Density Estimation Module
signals dynamically. The Monitoring Interface provides real-time traffic data to
authorities for manual control. Data Storage enables long-term analysis to enhance
system efficiency. This architecture ensures the system adapts traffic signal timings
according to real-time conditions, improving traffic flow and safety.
Figure 2
[0023] The system seeks to enhance traffic flow by adaptively adjusting wait time at
signals prevailing traffic volume. It employs YOLO va for object detection to identify and
10 count vehicles, while Deep Q-Learning, a reinforcement learning technique, is used to
determine optimal signal timings. This advanced object detection model processes
video frames to accurately detect and count vehicles.
[0024] A reinforcement learning algorithm that learns the optimal policy for traffic signal
15 control. Placed at intersections to capture real-time traffic data. GPUs for training the
YOLO model and CPUs for running the trained model in real-time. Collect video footage
from traffic cameras. Label the data with vehicle types and positions to train the YOLO
model. Preprocessing: Convert video frames to a suitable format for YOLO and
normalize the data.
20
[0025] YOLO v8 segments the image into multiple grids, forecasting bounding boxes
and determining the likelihood of classes within each grid section. Annotated data is
used to train this model, considering loss functions like classification, localization, and
confidence loss to ensure high accuracy. The model is validated against a separate
25 dataset, followed by deployment ·in real-time traffic monitoring systems where it
(
processes live video streams to detect and count vehicles. The results are then
transmitted to the traffic control system for further action.
[0026] Define the state as the number of vehicles in each lane. Possible actions include
30 changing the signal for each direction (e.g., green, yellow, red). Design a reward
function to minimize waiting time and congestion. For example, a negative reward for
long queues and a positive reward for smooth traffic flow. The developed a neural
network model estimate the Q-values once the collected data is used to train the QZ
o network. by jnteragting
a-values based on the reward received. Create a simulated traffic environment to test
the system.
[0027] Train the Deep a-Learning agent in the simulation before deploying it in the real
world. Test the system under various traffic conditions to ensure robustness. Real-time
5 Operation: Deploy the system at intersections. The YOLO model processes video
feeds, and the Deep a-Learning agent adjusts the traffic signals in real-time.
Monitoring and Maintenance: _Continuously monitor the system's performance and
retrain the models as needed to adapt to changing traffic patterns.
10 LIST OF REFERENCE NUMERALS:
100 - Capturing vehicles
101- CCTV earner103- Processor
15 1 04 - Signal output
105 - Overview

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