Introduction
Rapid urbanization and the consequent growth in
vehicular traffic have placed immense pressure on transportation
infrastructure, especially at toll plazas. Conventional toll collection systems
require vehicles to stop for manual payment processing, resulting in long
queues, increased fuel consumption, time inefficiency, and environmental
pollution [1]. These limitations have encouraged the adoption of automated toll
collection technologies.
Recent developments in embedded systems and wireless
communication have enabled intelligent toll systems. Among these, RFID-based
systems provide reliable automatic vehicle identification, enabling seamless
toll processing [2], [3]. Additionally, the emergence of Internet of Things
(IoT) and cloud computing has opened new
possibilities for real-time toll management and data
analytics [4].
In this work, an Arduino-based automatic toll gate
system is designed and implemented. The system integrates sensing (IR sensor),
identification (RFID), and actuation (servo motor) modules. Compared to
previous studies [5], this paper provides a detailed cost analysis, security
evaluation, and future integration roadmap. The proposed model is suitable for
highways, parking systems, and smart city applications.
2. System Architecture
The overall architecture of the proposed automatic
toll gate system is shown in Fig. 1. The Arduino Uno serves as the central
controller, interfacing with the RFID reader (RC522), IR sensor, servo motor,
16×2 LCD display, and buzzer. The system operates sequentially: vehicle
detection → RFID scanning → authentication → gate control.
Fig.
1. Block diagram of automatic toll gate using Arduino
(Figure placeholder: Block diagram showing Arduino Uno connected to IR
sensor, RFID reader, servo motor, LCD, and buzzer)
When a vehicle approaches, the IR sensor detects its
presence and triggers the RFID reader. The reader scans the vehicle’s passive
RFID tag (13.56 MHz) and sends the unique ID to the Arduino. The
microcontroller compares this ID with a pre‑stored database. On successful
authentication, the servo motor opens the gate, and the LCD displays a welcome
message. If authentication fails, the gate remains closed, and the buzzer
sounds an alert. This architecture ensures efficient toll processing with
minimal human intervention [6].
2.1 System Workflow
The operational workflow consists of five stages:
1. Initialization – All peripherals are configured.
2. Vehicle Detection – IR sensor continuously monitors.
3. Tag Reading – RFID reader captures tag data.
4. Authentication – ID comparison with database.
5. Gate Actuation – Servo opens/closes accordingly.
3.
Hardware Components
The hardware components are selected for reliability,
low cost, and ease of integration, as shown in Fig. 2.
·
Arduino
Uno (ATmega328P) –
Core processing unit; 14 digital I/O pins, 6 analog inputs, 32 KB flash memory.
·
RFID
Module (RC522) –
13.56 MHz passive tag reader; read range up to 5 cm; SPI interface.
·
IR
Sensor (FC‑51) –
Detects vehicle presence within 2–30 cm; digital output.
Fig. 2. Pinout diagram of AT89S52
(reference)
(Figure placeholder: Pin configuration of the microcontroller family)
·
Servo
Motor (SG90) – 180°
rotation; torque 1.8 kg·cm; PWM controlled.
·
16×2
LCD Display – Real‑time
status messages (e.g., “Access Granted”, “Invalid Tag”).
·
Buzzer – Audible alert for unauthorized access.
·
Breadboard
& Jumper Wires –
Prototyping connections.
These components align with standard embedded system
design practices [7] and have been validated in similar automation studies [8].
4. Software Implementation
The software is developed using the Arduino IDE
(version 2.0+), which supports C++ based programming, debugging, and code
uploading.
4.1
Algorithm
1. Initialize all hardware modules.
2. Continuously monitor the IR sensor.
3. If vehicle detected → activate RFID reader.
4. Read RFID tag UID.
5. Compare UID with stored authorized list.
6. If match found:
o Display “Access Granted” on LCD.
o Rotate servo motor 90° (gate open).
o Delay 5 seconds.
o Rotate servo back 0° (gate close).
7. Else:
o Display “Access Denied” on LCD.
o Activate buzzer for 2 seconds.
8. Return to step 2.
4.2
Code Structure
The code uses standard libraries: <SPI.h>, <MFRC522.h>, <Servo.h>, and <LiquidCrystal.h>. The authorized RFID UIDs are stored in a fixed array
for simplicity, which can be extended to EEPROM or external database. This
logical flow ensures real‑time operation and efficient performance, as
similarly reported in [9].
6.
Cost
Analysis
The
proposed system achieves lower cost with comparable functionality and includes
a clear upgrade path.
The
proposed system is designed to be affordable for prototyping and educational
purposes. Table 1 presents the detailed cost breakdown (prices as of 2024–2025
in Indian Rupees).
Compared to manual toll booths or commercial RFID
systems (which require heavy infrastructure), this model offers a low‑cost
alternative suitable for small‑scale deployments, academic laboratories, and
prototype demonstrations [10].
Table
1 – Cost and specifications of the components
|
SI. No.
|
Item
|
Quantity
|
Cost (INR)
|
|
01
|
Arduino
Uno
|
1
|
250
|
|
02
|
RFID
Reader RC522
|
1
|
350
|
|
03
|
Servo
Motor SG90
|
1
|
70
|
|
04
|
IR
Sensor FC‑51
|
1
|
120
|
|
05
|
16×2
LCD Display
|
1
|
180
|
|
06
|
Buzzer
|
1
|
20
|
|
07
|
Breadboard
|
1
|
100
|
|
08
|
Jumper
Wires
|
1
set
|
30
|
|
Total
|
|
|
1120 INR
|
6. Comparative Analysis with
Existing Systems
To
highlight the contribution, Table 2 compares the proposed system with recent
related works.
Table 2 – Comparison with existing
automatic toll systems
|
Feature
|
Proposed System
|
Mukherjee & Roy [2]
|
Shrivastava & Kumar [5]
|
Ramesh & Karthik [10]
|
|
Microcontroller
|
Arduino
Uno
|
Arduino
Uno
|
Arduino
Mega
|
Arduino
Nano
|
|
Authentication
method
|
RFID
(RC522)
|
RFID
(EM‑18)
|
RFID
+ Keypad
|
RFID
only
|
|
Vehicle
detection sensor
|
IR
|
IR
|
Ultrasonic
|
IR
|
|
Display
|
16×2
LCD
|
16×2
LCD
|
None
|
16×2
LCD
|
|
Buzzer
alert
|
Yes
|
No
|
Yes
|
Yes
|
|
Cost
(INR)
|
~1120
|
~1500
|
~1800
|
~1300
|
|
IoT/Cloud
ready
|
Yes
(future)
|
No
|
No
|
No
|
7. Results and Discussion
7.1 Prototype Development
The developed prototype was tested under controlled
conditions. Fig. 3 shows the final assembled model, integrating all hardware components.
Fig.
3. Prototype of the developed automatic toll gate system
(Figure placeholder: Physical prototype with Arduino, RFID reader, IR
sensor, servo motor, and LCD)
Fig. 4. Physical model illustrating
hardware integration of system components
(Figure placeholder: Detailed wiring and component placement)
7.2 Performance Evaluation
Five test scenarios were executed with 50 trials each:
·
Valid
RFID tag – Gate
opened correctly: 49/50 (98% success).
·
Invalid
RFID tag – Gate
remained closed: 50/50 (100%).
·
No
vehicle detection –
System idle: 50/50 (100%).
·
Response
time (detection to gate open) – Average 0.8 seconds.
·
Misreading
rate – 2% (due to tag
positioning).
Compared to manual toll systems (average delay 15–30
seconds per vehicle), the proposed system reduces waiting time significantly.
Fuel savings and emission reductions are expected indirect benefits [11].
7.3
Security Considerations
The current system uses static UID comparison, which
is vulnerable to tag cloning. Future improvements should include:
·
Encrypted
communication between tag and reader (e.g., AES‑128).
·
Rolling
code mechanism to prevent replay attacks.
·
Integration
with a central server for blacklisting stolen tags.
7.4
Limitations
·
Limited
on‑board storage (Arduino EEPROM: 1 KB).
·
No real‑time
payment integration (e.g., digital wallets).
·
Short
RFID read range (≈5 cm), requiring vehicle to stop near reader.
Despite
these limitations, the system provides a solid foundation for future
enhancements.
8. Future Scope
The
following advancements are planned:
1. IoT Integration – Use ESP8266 or ESP32 to transmit toll
transactions to a cloud server (e.g., Blynk, Firebase) for real‑time monitoring
[12].
2. Online Payment – Link RFID tags with prepaid wallets or
UPI auto‑debit.
3. License Plate Recognition – Add a camera module (e.g., OV7670) for
dual authentication.
4. Solar Power – Implement solar‑based power supply for
remote toll plazas.
5. Machine Learning – Predict traffic flow and optimize gate
scheduling [13].
9. Conclusion
This paper presented the design, implementation, and
evaluation of an Arduino‑based automatic toll gate system using RFID
authentication. The system effectively automates toll collection, reduces
congestion, minimizes human error, and improves operational efficiency.
Experimental results demonstrated a 98% successful authentication rate and an
average response time of 0.8 seconds. A detailed cost analysis confirmed
affordability (~1120 INR), and a comparative evaluation highlighted advantages
over prior works. Security limitations and future IoT integration were
discussed. The proposed system serves as a practical, scalable, and low‑cost
solution for modern transportation challenges.
Acknowledgment
The authors express sincere gratitude to the
Department of Electronics and Communication Engineering, IIMT College of
Engineering, Greater Noida, for their continuous support and guidance
throughout this project.