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Int. j. inf. tecnol.
https://doi.org/10.1007/s41870-023-01527-w
ORIGINAL RESEARCH
Cloud‑connected central unit fortraffic control: interfacing
sensing units andcentralized control forefficient traffic
management
RahulGoyal1· OjasElawadhi1· AkshatSharma1·
MonicaBhutani1 · ArohiJain1
Received: 9 June 2023 / Accepted: 11 September 2023
© The Author(s), under exclusive licence to Bharati Vidyapeeth’s Institute of Computer Applications and Management 2023
congestion, and enhancing transportation efficiency. Experi-
mental validation is required to evaluate the system’s per-
formance, but the conceptual design and prototype highlight
promising prospects for improving traffic control while mini-
mizing delays and optimizing overall traffic management.
Keywords Traffic management system· Ultrasonic
sensor· Temperature calibration· HCSR-04· BMP280·
Accuracy· Distance measurement· Communication
protocol· Central unit· Sensing units
1 Introduction
Efficient traffic management is critical to maintaining
smooth transportation systems and minimizing congestion
on roadways. The optimization of traffic flow and the reduc-
tion of delays play a vital role in improving overall trans-
portation efficiency and ensuring public safety. Traditional
traffic control systems based on fixed timings often fail to
adapt to real-time traffic conditions, leading to inefficient
traffic management and increased congestion.
Camera-based traffic monitoring systems have been
explored as an alternative solution to address these chal-
lenges. However, they come with their own set of limita-
tions. Camera-based systems are often costly to implement
and maintain, requiring significant infrastructure invest-
ments and ongoing expenses for installation, maintenance,
and power supply. Furthermore, these systems are suscep-
tible to theft and vandalism, making them less reliable and
prone to downtime.
Moreover, implementing camera-based systems at a
large scale poses additional logistical challenges, requiring
a vast network of cameras, extensive cabling, and complex
data processing infrastructure. The costs and complexities
Abstract Efficient traffic management is crucial for ensur-
ing smooth transportation systems and minimizing conges-
tion on roadways. This research paper proposes a novel
approach to traffic control by developing a cloud-connected
central unit that interfaces with sensing units to effectively
manage traffic light timings based on real-time density infor-
mation. The central unit, built using modern technology,
integrates ultrasonic sensor-equipped sensing units distrib-
uted along the road segment. These sensing units continu-
ously measure the density of vehicles and transmit the data
to the central unit through a local area network (LAN), opti-
mizing data transmission efficiency. The central unit lever-
ages intelligent algorithms to analyze the traffic density pat-
terns and dynamically adjusts the timing of traffic lights to
optimize traffic flow. Additionally, the central unit provides
a web-based interface accessible to authorized administra-
tors and traffic police, enabling them to monitor and control
the system effectively. The implementation of this system
demonstrates the potential of utilizing cloud connectivity
and advanced algorithms for traffic management, reducing
* Monica Bhutani
shiny.mona@gmail.com
Rahul Goyal
rhgoyal01@gmail.com
Ojas Elawadhi
ojaselawadhi1@gmail.com
Akshat Sharma
akshatsharmaishu@gmail.com
Arohi Jain
arohij15@gmail.com
1 Electronics andCommunication Engineering Department,
Bharati Vidyapeeth’s College ofEngineering, NewDelhi,
India
Int. j. inf. tecnol.
1 3
associated with camera-based systems limit their feasibility
for widespread deployment and effective traffic management
across a city or region.
To overcome these challenges, this research paper pro-
poses a novel approach to traffic control through the develop-
ment of a cloud-connected central unit that interfaces with
sensing units for real-time traffic monitoring and adaptive
traffic light control. Unlike camera-based systems, the pro-
posed system utilizes ultrasonic sensor-equipped sensing
units distributed along the road segment, eliminating the
need for costly cameras and complex infrastructure.
By leveraging modern technology and intelligent algo-
rithms, the central unit analyzes the density of vehicles
captured by the ultrasonic sensing units and dynamically
adjusts the timing of traffic lights accordingly. The use of
ultrasonic sensors reduces implementation costs, simplifies
maintenance, and eliminates the risks associated with theft
and vandalism. This makes the proposed system more practi-
cal, scalable, and suitable for large-scale deployment across
diverse road networks.
In addition to the cost and maintenance advantages, the
cloud connectivity of the central unit provides further ben-
efits. It enables real-time data transmission and analysis,
facilitating efficient traffic management and enabling author-
ized administrators and traffic police to monitor and control
the system remotely. The web-based interface offers a user-
friendly platform for system management, reducing response
time and enhancing overall traffic control capabilities.
Through experimental validation, the performance and
effectiveness of the proposed system will be evaluated,
showcasing its potential benefits in terms of traffic flow opti-
mization, congestion reduction, and improved transportation
efficiency. Furthermore, the research paper will discuss the
scalability, reliability, security, and privacy considerations
associated with the proposed system, addressing the chal-
lenges faced by camera-based systems (Fig.1).
2 Literature Survey
A camera-based traffic management system comprises three
key components: a camera unit, a sensing unit, and a micro-
controller unit that acts as the system’s central processing
unit. This system emerged as a solution when manual traf-
fic management and fixed-timing red light systems proved
inadequate in handling the growing number of vehicles and
traffic congestion. By leveraging Artificial Intelligence algo-
rithms, this technology captures multiple images of road-
ways and employs Convolutional Neural Network (CNN)
techniques for vehicle detection and counting.
The camera unit continuously captures traffic images,
which are then analyzed by the microcontroller unit. The
CNN-based algorithms enable precise vehicle detection,
facilitating an accurate count of the total vehicles on the
road. This information is computed for each street within
a section, generating a score that reflects the traffic levels.
The system utilizes this score to allocate specific timing
for traffic lights based on the vehicle count and congestion
levels.
To develop an efficient traffic management system,
extensive research has been conducted to address vari-
ous challenges encountered by existing schemes. Multiple
state-of-the-art approaches have been explored, focusing
on implementability, manageability, and effectiveness. The
research paper presents a comprehensive table summariz-
ing different papers and the approaches they employed,
ensuring a comprehensive understanding of the diverse
techniques applied in traffic management (Table1).
Our comprehensive literature survey delved into Mobile
Ad hoc Networks (MANETs), encompassing Ad Hoc
Network dynamics, tailored data reduction methods, and
strategies to counter malicious nodes. Notably, Bandana
Mahapatra and Srikant Patnaik’s work on data reduction
techniques for MANETs aligned seamlessly with our
goal of optimizing data flow, while Tauseef Jamal and
Shariq Aziz Butt’s meticulous analysis of malicious node
behavior fortified the security framework of our proposed
system.
These seminal contributions collectively enhance our
research’s credibility and lay a robust foundation as we
advance toward a secure, technologically adept solution
for addressing urban traffic challenges through MANETs.
With insights drawn from these works, we aim to establish
our approach as a pivotal building block in the complex
tapestry of today’s urban traffic management.
Fig. 1 Graphical representation of Traffic and Roads in Urban Areas
Int. j. inf. tecnol.
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Table 1 Related research work
References Findings
R. Goyal, A. Sharma, A. Jain, O. Elawadhi and M. Bhutani [1] The above work can be summarized as an endeavor to achieve a low-
cost, effective, and simple-to-implement traffic management system
which can be applied nationwide so that in every part of the country
and world, better traffic management can be done. This work also tar-
gets making a road journey quick and safe by managing intersectional
traffic. With this proposed work, the aim is to make the traffic system
completely autonomous
C. Tang, W. Hu, S. Hu, and M. E. J. Stettler [2] In contrast to other traffic control systems, this framework makes full
use of the findings from research on traffic flow prediction to predict
the level of traffic congestion at a future time interval. This gives
ample time to plan the timing of signals or take other actions to reduce
congestion
C. -Y. Jiang, X. -M. Hu and W. -N. Chen [3] In contrast to other traffic control systems, this framework makes full
use of the findings from research on traffic flow prediction to predict
the level of traffic congestion at a future time interval. This gives
ample time to plan the timing of signals or take other actions to reduce
congestion
Yusuf and Yusuf [4] This paper suggested focusing on modeling based on the Unity3D plat-
form using fuzzy logic control and an agent-based modeling system to
control traffic lights
Akhtar etal. [5] This paper describes the creation of an IoT-based dynamic traffic
management system that is based on congestion level. It controls the
length of the traffic lights based on the level of traffic congestion that
is being measured in real-time at road crossings using ultrasonic sen-
sors
N. T. Thu Hien, N. Viet Hung, H. D. Khanh, T. N. Son and N. T.
Dzung [6]
This paper’s findings, traffic density analysis for adaptive traffic signal
control, and flow entering the crossroads as recorded by the camera.
To improve traffic light control, the proper uptime of the traffic signal
can be estimated in real-time
S. U. Dampage, T. D. Munasingha, W. D. K. Gunathilake, A. G.
Weerasundara and D. P. D. Udugahapattuwa [7]
The suggested paper makes use of live video as an input and outputs
adaptive phase timings. They developed a per-car unit (PCU) as a
novel input to express each vehicle type’s impact on the state of the
traffic. Their suggested technique offers an increase in mean speed for
both a single junction and a scenario involving multiple junctions
K. Dubey and T. Gupta [8], This paper tells about the present-day scenario of traffic control systems
and solutions by making use of technology to improve the existing
conventional models. There are numerous technological advancements
in ATCS discussed in the paper like ITACA, SCAT, RHODES, and
CoSiCoSt. They have the potential to improve extreme traffic conges-
tion in cities, decrease commuter travel time and reduce the number of
hours spent stuck in traffic
M. F. AbdelHaq and A. Salman [9] In this paper, they created a Wireless Sensor Network (WSN) applica-
tion based on roadside ARM magnetic sensors for vehicle counting,
vehicle classification, and speed measurement
YOLO [10] You Only Look Once is a real-time traffic light control algorithm that
monitors traffic flow by real-time object detection using deep convolu-
tional neural networks (YOLO)
Kao and Wu [11] A machine-learning model for an adaptive traffic light control system
was proposed by in this paper. Their model consistently outperforms
the FTLCS in terms of overall waiting time and maximum road occu-
pancy after simulating various traffic levels
B. Pratama, J. Christanto, M. T. Hadyantama and A. Muis [12] This paper suggests using road pattern-based traffic density calculations
to regulate the timing of adaptive traffic lights. Image processing on
various road patterns is used to determine the traffic density. Later,
the data is gathered and the traffic light operations at a crossroads are
managed by a server
Int. j. inf. tecnol.
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3 Technical requirements andchallenges
inthepresent scenario
This system necessitates an expensive and high-maintenance
technical infrastructure. It requires high-speed microcon-
trollers capable of implementing real-time Convolutional
Neural Networks and Artificial Intelligence algorithms for
efficient traffic management. Clear and sharp images are
essential, and specific cameras may need additional equip-
ment for night-time operation, which adds to the cost. Addi-
tionally, the system is susceptible to theft due to the high
value of the equipment involved. While adaptive traffic light
Table 1 (continued)
References Findings
A. Elmrini and A. G. Amrani [13] Their research centered on creating a method to control road traffic
in an environment of smart cities by using wireless sensor networks
as a way to monitor the traffic. In this study, they offered a series of
solutions to reduce traffic jams and enhance traffic flow. The proposed
countermeasures were put through a simulation, and the results dem-
onstrated how our method decreased traffic congestion, decreased gas
emissions, and improved fuel economy
A. Mishra, K. Chen, S. Poddar, E. Posadas, A. Rangarajan, and S.
Ranka [14]
This paper presents state-of-the-art approaches to identify conflict
hotspots in vehicle-vehicle and vehicle–pedestrian conflict hotspots.
They provide a novel method for conflict hotspot discovery, volume
hotspot detection, and intersection-service evaluation that enables us
to comprehend performance and safety concerns and choose the best
defenses
S. Vergis, V. Komianos, G. Tsoumanis, A. Tsipis, and K. Oikonomou
[15]
This paper presented an IoT-based and fog computing-based low-cost
vehicle traffic monitoring system. A three-tiered architecture made
up of contemporary hardware serves as the foundation for the offered
VTS. The system is made up of MTS devices, which are mounted
on vehicles and use GPS technologies to collect records about their
locations, IGS devices, which collect the data collected by the MTS
devices, and FDs, which process the data and upload it to a server so
that it can be accessed and used remotely
Alkhatib, A.A. [16] This research presents a simple low-cost technique for gathering data
for traffic control systems that uses inexpensive infrared sensors and
timers
Goel, S., Singh, J.B. and Sinha, A.K [17] An interdependent relationship between a city’s transportation infra-
structure and socioeconomic structure leads to better transportation
infrastructure, which in turn affects socioeconomic development.
Demand for transportation is created as civilization develops. The tra-
ditional techniques for planning transportation are based on straight-
forward trend extrapolation
Choudhary, P., Dwivedi, R.K. and Singh, U. [18] Environmental degradation, excessive resource and energy consumption
in big centers are only a few of the numerous issues brought on by the
transformation brought on by the world’s move toward urbanization.
In many places, the lengthening of commutes because of high traffic is
becoming a major issue
Jamal, T. and Butt, S.A. [19] The mobile ad hoc networks (MANETS) are multi-hop, decentralized
networks in which intermediate nodes act as routers to send data
packets to their intended locations. Due to mobility and the constantly
changing topology of MANETS, routing protocols are essential to
their efficacy
Rajagopal, B.G. [20] Security and monitoring are becoming an essential components of daily
life. To ensure safety at all levels, the Smart Cities that are now being
constructed require an intelligent surveillance system. The purpose of
this research is to present an integrated framework for vehicle recogni-
tion and classification using real-time traffic-related video
Mahapatra, Bandana, and Srikant Patnaik [21] A set of nodes that join together to form an ad hoc network are defined
as having scarce or restricted resources, such as energy, memory
capacity, etc., with each node autonomously contributing to all
network-related operations. It becomes extremely difficult to maintain
robust and attack-free connectivity between these nodes
Int. j. inf. tecnol.
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management systems utilizing cameras for vehicle detection
and traffic regulation have been proposed in the past, they
often encounter cost-related challenges and rely heavily on
camera-based technologies for traffic analysis and control
(Fig.2).
3.1 High installation, maintenance, andfailure cost
The system incurs significant expenses in terms of installa-
tion, maintenance, and potential failures. Natural calamities
such as floods and tsunamis pose a severe risk, leading to
the complete destruction of camera systems and increasing
the likelihood of system failures.
3.2 Costly items andissue ofrobbery
The cameras and associated equipment used in the system
are expensive, making them susceptible to theft. This poses
a liability for authorities who must ensure the security of
these physical components.
3.3 Challenges innationwide implementation
Due to the substantial costs associated with installation and
maintenance, implementing the system on a national scale
becomes financially burdensome and practically unfeasible
(Fig.3).
In the previous Research Paper [1], a comprehensive traf-
fic management system was designed, taking into consid-
eration the specific requirements of the road network. The
system was logically defined to incorporate six sensors, with
three sensors placed on each side of the road, along with a
central unit responsible for controlling the traffic lights.
The placement of sensors on both sides of the road
allowed for effective monitoring of the traffic flow and den-
sity. By strategically positioning the sensors, the system
could capture data from multiple points, providing a more
accurate representation of the traffic conditions.
One notable aspect of the previous was the exploration
of the Rodem algorithm. This algorithm offered a novel
approach to traffic management by utilizing ultrasonic
sensors without the need for computer vision. The Rodem
algorithm focused on analyzing the ultrasonic sensor data to
determine and manage traffic flow effectively.
By leveraging the data from the ultrasonic sensors, the
algorithm could make intelligent decisions regarding traf-
fic light timings and optimize the overall traffic manage-
ment process. This approach presented a cost-effective
and efficient alternative to computer vision-based systems,
as it eliminated the need for complex image processing
algorithms.
The previous paper laid the foundation for the develop-
ment of the traffic management system, providing a logical
framework that combined sensor placement, data analy-
sis, and traffic light control. The exploration of the Rodem
algorithm showcased the potential for leveraging ultrasonic
sensors to achieve accurate and reliable traffic management
without relying on computer vision technology (Fig.4).
In conclusion, the current state of traffic management sys-
tems utilizing cameras presents notable technical challenges.
The system demands costly infrastructure, high-speed
microcontrollers, and precise image quality. Additional
financial burdens arise from expensive night equipment and
the risk of theft. Moreover, the high costs associated with
installation and maintenance, along with the complexity of
implementing the system on a large scale, further contribute
Fig. 2 Conventional Traffic Management System based on wired
ultrasonic sensors and raspberry pi [6]
Fig. 3 Prototype of conventional smart Traffic Management System
based on camera [10]
Int. j. inf. tecnol.
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to the hurdles faced in this domain. We aim to implement the
proposed traffic management system practically, leveraging
strategically placed sensors and the Rodem algorithm for
effective traffic control.
4 Cloud‑connected central unit traffic
management scenario
Our traffic management scenario incorporates four essen-
tial components: hardware design, hardware firmware, user
experience/user interface, and website structure, ensuring
efficient and user-friendly traffic control and management.
The components are as follows:
4.1 Hardware design
The sensing unit of the traffic management system incor-
porates a cost-effective Printed Circuit Board (PCB) design
that efficiently integrates various components. The main
microcontroller unit, the Amica ESP8266 development
board, acts as the central processing unit, handling data
processing and communication tasks.
The PCB design includes the HCSR-04 ultrasonic sen-
sor, which accurately detects vehicle density on the road
segment. Additionally, the BMP280 temperature sensor is
integrated to correct ultrasonic sensor values by compensat-
ing for temperature variations, ensuring precise and reliable
readings.
To provide power to the sensing unit, a dedicated power
supply module is integrated on the PCB, efficiently convert-
ing the main power source of 250V to a stable5V supply
required by the microcontroller and other components. This
power supply module ensures reliable and consistent opera-
tion of the sensing unit.
The PCB design features an LED indicator that serves
as a power-on status indicator, providing a visual indication
of the unit’s operational state. This LED enables users to
quickly determine if the sensing unit is powered and func-
tioning properly.
For convenient and secure power and ground connections,
the PCB incorporates 01 × 02 screw terminal connectors.
These connectors facilitate easy and reliable attachment of
power and ground wires, ensuring stable and robust electri-
cal connections within the sensing unit.
To enhance performance and reliability, the PCB design
includes a fill zone that optimizes the flow of returning cur-
rents. This fill zone minimizes electromagnetic interference
and promotes proper grounding, contributing to the overall
stability and functionality of the sensing unit (Fig.5).
The cost-effective PCB design for the sensing unit inte-
grates the Amica ESP8266 development board, HCSR-04
ultrasonic sensor, BMP280 temperature sensor, power sup-
ply module, LED indicator, screw terminal connectors, and
fill zone. This well-designed PCB ensures efficient compo-
nent integration and reliable operation of the sensing unit in
the traffic management system, while keeping production
costs affordable.
4.2 Firmware
The system consists of two essential components: the sens-
ing unit and the central unit. The sensing unit is responsible
for data collection, utilizing hardware components such as
Ultrasonic Sensor Units. The firmware within the sensing
unit processes and transmits this data to the central unit.
Sensing Unit: The firmware used in the sensing unit con-
nects to the central unit’s Access Point and sends data via
GET requests with headers. It incorporates the HCSR-04
library for distance measurement and temperature correc-
tions from the BMP280 library. The firmware utilizes the
AsyncWebServer library for establishing a connection and
sending data asynchronously.
By leveraging the HCSR-04 and BMP280 libraries, the
firmware ensures accurate distance measurements with tem-
perature corrections. The AsyncWebServer library enables
seamless communication with the central unit, facilitating
reliable data transmission and connectivity.
Fig. 4 Blueprint of Proposed Smart Traffic Management System
based on RODEM algorithm and Road Division [1]
Fig. 5: 3D prototype of an ultrasonic sensor unit
Int. j. inf. tecnol.
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The firmware code establishes a connection with the
Access Point, sends data using GET requests with headers,
and utilizes libraries for distance measurement and tempera-
ture corrections. This enables efficient communication and
accurate data transmission between the sensing unit and the
central unit.
Central Unit: The firmware in the central unit of the traf-
fic management system combines both Station (STA) and
Access Point (AP) modes to enable dual functionality. In
STA mode, the central unit connects to the cloud, allowing
seamless integration with cloud services for data storage,
analysis, and remote management. In AP mode, it acts as
an Access Point, providing a connection interface for the
sensing units to connect and send data via asynchronous
Transmission Control Protocol (TCP).
By combining STA and AP modes, the firmware in the
central unit ensures a comprehensive and versatile solution.
It establishes a secure connection to the cloud, facilitating
data transfer and enabling administrators and traffic police
to access the system remotely. Simultaneously, it serves as
a hub for the sensing units, allowing them to connect and
transmit data via asynchronous TCP, ensuring efficient and
reliable communication.
The firmware’s implementation of STA mode ensures
seamless connectivity to the cloud, enabling administrators
and traffic police to monitor and manage the traffic man-
agement system through cloud-based services. At the same
time, the AP mode establishes a local network, facilitating
direct communication between the central unit and the sens-
ing units, streamlining data transmission and enhancing sys-
tem performance.
Through this firmware design, the central unit acts as a
bridge between the cloud and the sensing units, enabling the
system to benefit from cloud connectivity while providing a
user-friendly interface for the sensing units to connect and
send data. This integrated approach ensures efficient data
management, real-time monitoring, and effective control of
the traffic management system.
4.3 User experience anduser interface
The web console for administrators and traffic police offers
a user-friendly interface for efficient monitoring and control
of the traffic management system. With two pages designed
for optimal user experience, the console simplifies system
management tasks.
The login page ensures secure access by requesting
unique credentials from administrators and traffic police.
Once authenticated, users are directed to the second page,
which provides a comprehensive overview of the sys-
tem’s status and control options. This page utilizes intui-
tive visuals, such as boxes representing road segments and
sub-boxes symbolizing individual sensors, to display real-
time information.
By presenting binary values, ‘1’ for vehicle detection
and ’0’ for no detection, the console allows users to quickly
identify traffic flow and congestion patterns. This visual rep-
resentation enhances monitoring capabilities and enables
prompt actions to address any issues.
The second page also features a control panel, empower-
ing administrators to send real-time commands for traffic
flow management. By selecting specific road segments or
sensors, users can make customized adjustments to optimize
traffic flow and alleviate congestion (Fig.6).
To ensure system security, a logout option is provided,
allowing users to securely exit the console. This protects
sensitive data and prevents unauthorized access.
The web console prioritizes user experience by offering a
seamless and intuitive interface. It streamlines tasks through
a secure login, visual representations of traffic data, an inter-
active control panel, and a convenient logout option. This
enhances the efficiency of system management for adminis-
trators and traffic police.
The MCU unit reads values from the sensor, decides
whether the object is present, and generates the score to be
a fixed value or 0 based on the read values. It is also mainly
responsible for interfacing the sensing and central units
using a communication protocol that completes the system.
4.4 Website structure
The user interface (UI) of the traffic management system’s
webpage is professionally designed using industry-standard
practices, utilizing Vanilla JS and Vanilla
CSS for optimal implementation. The HTML code forms
a structured foundation, incorporating login forms, input
fields, buttons, and UI components to facilitate seamless
user interaction and efficient data handling.
Complementing the HTML structure, the meticulously
crafted CSS code enhances visual aesthetics and user
Fig. 6 The user interface of the Web App
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experience. Through precise styling, color schemes, and
layout configurations, the CSS code establishes a visually
appealing and cohesive design language, ensuring an intui-
tive and captivating interface.
JavaScript, implemented through Vanilla JS, adds inter-
activity to the webpage. GET requests, utilizing headers
for data retrieval and transmission, enable real-time access
to data and empower administrators and traffic police with
precise control over the traffic management system. This
integration of industry-standard practices, Vanilla JS, and
Vanilla CSS results in a robust and professional UI, pro-
viding a visually appealing, intuitive, and highly functional
interface for efficient traffic control and management.
5 Technical infrastructure requirements
Our developed system aims to minimize installation and
maintenance costs, making it feasible for large-scale imple-
mentation, such as nationwide deployment, while utilizing
simple and cost-effective components. The primary objec-
tive is to achieve optimal results with minimal technical
infrastructure. The subsequent sections provide an overview
of the required hardware and software infrastructures for
implementing our proposed system.
5.1 Hardware
In the central node of the traffic management system, the
hardware components include the following:
Amica Board: The Amica board serves as the main micro-
controller unit in the central node, handling data processing
and communication tasks.
Power Supply: A power supply module is used to provide
the necessary power to the central node. It ensures stable and
reliable operation of the components.
LED Indicator: An LED indicator is incorporated to
provide a visual indication of the operational status of the
central node. It helps users quickly identify the functioning
state of the system.
01 × 02 Screw Terminal Connector: The screw terminal
connector is utilized to facilitate convenient and secure elec-
trical connections for power and ground.
Traffic Light Control Header Pins: These header pins are
specifically designed for controlling the traffic lights. Each
pin corresponds to a specific aspect of the traffic light sys-
tem, such as the red, yellow, and green lights, allowing for
precise control and coordination of the traffic flow.
Each of these components plays a crucial role in the func-
tioning and operation of the central node in the traffic man-
agement system (Fig.7).
The hardware components used in the sensing node of the
traffic management system include the following:
Amica ESP8266 Development Board: This microcon-
troller unit serves as the central processing unit of the sens-
ing node, handling data processing and communication
tasks.
HCSR-04 Ultrasonic Sensor: The ultrasonic sensor is
responsible for measuring the distance between the sensor
and passing vehicles. It enables the system to determine the
density of traffic on the road segment accurately.
BMP280 Temperature Sensor: The temperature sensor
is integrated into the sensing node to correct the readings
obtained from the ultrasonic sensor. By compensating for
temperature variations, it ensures precise and reliable cal-
culations of traffic density.
Hi-Link Power Supply: The Hi-Link power supply mod-
ule is utilized to convert the main power source, typically
250V, to a stable5V supply required by the microcontroller
and other components. It ensures a consistent and reliable
power delivery to the sensing node.
LED Indicator: An LED indicator is incorporated into
the hardware design to provide a visual indication of the
power-on status of the sensing node. It allows for easy moni-
toring and quick identification of the operational state of the
system.
01 × 02 Screw Terminal Connectors: These connectors
provide convenient and secure connections for power and
ground wires within the sensing node. They ensure reliable
electrical connections, minimizing the risk of loose connec-
tions or electrical issues (Fig.8).
5.2 Networking andLAN
In the above system, both the central node and the sensing
units function as transceivers, allowing bidirectional com-
munication between them. This enables data transmission in
both directions, allowing the central node to send commands
and receive data from the sensing units, and vice versa.
The transceiver functionality in the central node and
sensing units is achieved through the use of appropriate
hardware and software components. These components
Fig. 7 Central node of the traffic management system
Int. j. inf. tecnol.
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enable the devices to both transmit and receive data sig-
nals, facilitating seamless communication within the
system.
By acting as transceivers, the central node can send
commands to the sensing units, such as adjusting traffic
light timings or requesting data updates. Simultaneously,
the sensing units can transmit real-time data to the cen-
tral node, providing information on traffic density, vehicle
detection, or any other relevant parameters.
This bidirectional communication capability enhances
the overall functionality and effectiveness of the traffic
management system. It allows for efficient coordination
between the central node and the sensing units, enabling
real-time monitoring, control, and optimization of traffic
flow.
The transceiver functionality ensures that the central
node and sensing units can exchange data and commands
seamlessly, facilitating the smooth operation of the traffic
management system and enabling effective traffic control
and management (Fig.9).
6 Performance analysis oftheproposed system
withstate‑of‑the‑art algorithms
We aim to enhance road management by implementing a
cost-effective and straightforward traffic management sys-
tem, contrary to common perception. The key aspect of
our system lies in its affordability and ease of deployment.
Instead of relying on camera-based vehicle counting and
regulation, our system primarily focuses on assessing traffic
density. In comparison to existing management systems, we
have successfully overcome various challenges to develop a
more efficient solution.
The use of inexpensive components and simple circuitry
in both the sensing units and central unit significantly
reduces installation and replacement costs while mitigating
the risk of theft due to the low-value materials. The pro-
posed system boasts easy installation, requiring minimal
labor and associated expenses. Moreover, the use of afford-
able and low-maintenance components eliminates the need
for frequent cleaning, maintenance, or replacement. In the
event of damage, the components are readily replaceable at
a minimal cost.
By employing multiple sensors, the system remains
functional even if one or two units are damaged, ensuring a
significantly reduced failure rate. Additionally, the system’s
remarkably low cost enables its implementation on a large
scale, including nationwide deployment.
7 Results anddiscussion
The following comparison provides insights into the accu-
racy of using the HCSR-04 ultrasonic sensor alone and using
the HCSR-04 sensor with the BMP280 temperature sensor
for correction:
7.1 HCSR‑04 ultrasonic sensor
Mean Accuracy: The HCSR-04 sensor, when used inde-
pendently, exhibits an average accuracy of ±5% in distance
measurements.
Variability: The measurements obtained solely from the
HCSR-04 sensor show a standard deviation of approximately
2.5%.
Environmental Influence: The accuracy of the HCSR-04
sensor is susceptible to temperature variations, which can
introduce errors in distance measurements.
7.2 HCSR‑04 withBMP280 temperature sensor
Mean Accuracy Improvement: By incorporating the BMP280
temperature sensor for temperature corrections, the average
accuracy of the distance measurements improves to ±3%.
Fig. 8 Sensing node of the traffic management system
Fig. 9 Block diagram representing networking scheme of proposed
system
Int. j. inf. tecnol.
1 3
Reduced Variability: The standard deviation of the
corrected distance measurements decreases to approxi-
mately 1.5%, indicating improved consistency and reduced
variability.
Temperature Compensation: The BMP280 sensor allows
for real-time monitoring of the ambient temperature, ena-
bling precise temperature compensation and enhancing
accuracy under varying environmental conditions.
The comparative analysis demonstrates that using the
HCSR-04 sensor with the BMP280 temperature sensor for
correction significantly improves accuracy and consistency
compared to using the HCSR-04 sensor alone. The accuracy
is enhanced by approximately 2%, reducing the variability
and minimizing the influence of temperature fluctuations on
the measurements. These findings highlight the efficacy of
incorporating the BMP280 temperature sensor for accurate
and reliable distance measurements in traffic management
applications (Table2).
8 Conclusion
In conclusion, the findings of this study demonstrate that
by incorporating temperature calibration using the BMP280
sensor, the readings of the HCSR-04 ultrasonic sensor were
significantly improved in terms of accuracy. The tempera-
ture correction mechanism effectively compensated for
temperature variations, resulting in more reliable distance
measurements.
Furthermore, the networking protocol employed in the
project was thoughtfully designed, allowing seamless com-
munication between the central hub and multiple sensing
units. This many-to-one protocol facilitated quick and effi-
cient data transmission, enabling real-time monitoring and
control of the traffic management system. The hardware
implementation of the project was executed with careful
attention to detail. The sensing units were skillfully sol-
dered and assembled, ensuring reliable functionality and
durability.
Overall, this research project successfully demonstrated
the benefits of temperature calibration for the HCSR-04
sensor and showcased a well-designed networking proto-
col for efficient data communication. The hardware imple-
mentation, including soldering and PCB design, played
a crucial role in the successful execution of the project.
These findings contribute to the field of traffic manage-
ment systems and pave the way for improved accuracy and
performance in traffic monitoring and control applications
(Figs.10, 11).
Table 2 A comparison of distance measurements obtained by the
HCSR04 sensor, both without temperature calibration and with tem-
perature calibration using the BMP280 sensor
Actual
distance (in
cm)
Distance measured
by HCSR-04 without
temperature calibration
(in cm)
Distance measured by
HCSR-04 with temperature
calibration (in cm)
31 29.5 31
61 59 60.5
91 89.5 92
121 116.8 119.5
151 148.4 150
Fig. 10 Graphical Representation of noise reduction using in HCSR-
04 reading using BMP280 temperature sensor for 31cm distance
Fig. 11 Graphical Representation of noise reduction using in HCSR-
04 reading using BMP280 temperature sensor for 91cm distance
Int. j. inf. tecnol.
1 3
9 Future scope
In future work, the study aims to manufacture a specialized
central unit and integrate it with a traffic controller to cre-
ate a complete traffic management system. The central unit
will be designed to optimize performance, reliability, and
cost-effectiveness. Integration with a traffic controller will
enable dynamic control of traffic lights based on real-time
sensor data, enhancing traffic flow optimization and con-
gestion management. Field testing and validation in real-
world traffic scenarios will provide valuable feedback for
system refinement and optimization. The future work aims to
develop a practical and efficient traffic management system
for improved road safety and optimized traffic flow.
Data availability The empirical data referred to in this paper are
available on request from the corresponding author, but are not public
due to privacy restrictions.
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