1.       Introduction

The continuous increase in personal and commercial vehicle usage has placed immense pressure on modern road transportation networks. Highways and expressways experience frequent congestion, particularly at toll plazas where manual toll collection methods are still prevalent. Traditional toll systems rely on cash transactions or card-based payments, which require human intervention and result in long vehicle queues, increased fuel consumption, higher emissions, and significant time loss. These inefficiencies collectively contribute to economic losses and environmental pollution, making toll management a critical issue in modern transportation infrastructure.

Conventional toll collection methods are also prone to human errors, such as incorrect toll calculation, transaction delays, and revenue leakage. As traffic density continues to rise, manual toll booths are increasingly unable to meet the demands of fast, accurate, and reliable vehicle processing. Consequently, there is a growing need for automated solutions that can streamline toll operations while ensuring accuracy, security, and scalability.

Technological advancements since the 1980s have led to the development of electronic and sensor-based tolling systems. Early approaches employed pressure sensors and analog detection techniques, which were later replaced by digital and wireless technologies capable of automated vehicle identification and monitoring [1–3]. Among these, Electronic Toll Collection (ETC) systems based on RFID technology have gained widespread acceptance due to their reliability and cost-effectiveness. RFID systems use dedicated readers and tags to identify vehicles uniquely and enable automatic toll deduction without requiring vehicles to stop at toll booths [4].

In recent years, the integration of IoT technology with toll collection systems has further enhanced operational efficiency. IoT-enabled systems allow real-time data transfer to cloud servers, facilitating centralized monitoring, transaction logging, and system diagnostics. Such connectivity improves transparency, enables remote supervision, and supports data-driven decision-making for traffic management authorities [5], [6].

Motivated by these advancements, this paper proposes a low-cost Automatic Toll Gate System using RFID and IoT technologies. The system is designed to automatically detect vehicles, deduct toll charges, control gate operation, and upload transaction data to a cloud platform for monitoring. The use of widely available microcontroller platforms and standard sensors ensures affordability and ease of deployment. By eliminating manual intervention and enabling real-time monitoring, the proposed system aims to reduce congestion, improve traffic flow, and support the development of smart transportation systems [7], [8].

Fig.1. Block diagram of Automatic Toll Gate System

2.    Architecture

The above figure 1, shows the architecture of the Automatic Toll Gate System. It consists of an ESP8266 microcontroller, an RFID Reader (instead of Load Cell), an Ultrasonic Sensor (instead of LPG sensor), an I2C Display, a Buzzer, and a 5 volt power supply [5], [8]. Additionally, a cloud server stores all information regarding toll transactions and vehicle IDs. The ESP8266 is selected due to its integrated Wi-Fi communication and less power consumption, making it suitable for IoT applications.

A.    Arduino-Uno Development Board

The Arduino Uno is a widely used open-source microcontroller board based on the ATmega328P microcontroller. It is popular in academic and hobbyist projects due to its simplicity, affordability, and extensive community support.

Table 1: Cost and specifications of the concept.

For object detection applications, the Arduino Uno serves as the central processing unit that interfaces with various sensors, in figure 2, (e.g., ultrasonic, infrared, or camera modules) to detect and measure the distance or presence of objects [5].

Its low power consumption, ease of programming, and compatibility with a wide range of sensors make the Arduino Uno an ideal choice for real-time, low-cost object detection systems in research and prototyping.

Key Features Relevant to Object Detection:

·         Microcontroller: ATmega328P, operating at 16 MHz

·         Memory: 32 KB Flash (0.5 KB used by bootloader), 2 KB SRAM, 1 KB EEPROM.

·         I/O Pins: 14 digital I/O pins (6 PWM), 6 analog inputs for sensor data acquisition.

·         Communication: Supports UART, I2C, and SPI for connecting multiple sensors and modules.

·         Power Supply: Operates at 5V (with 7–12V recommended input via barrel jack or USB).

·         Programming: Uses the Arduino IDE with C/C++-

·         For easy programming, Onboard USB-TTL converter based language, making it beginner-friendly yet powerful for research.

·        

Fig.2. Arduino Uno

B.    ULTRASONIC SENSOR

An ultrasonic sensor is a non-contact distance measurement device that uses high-frequency sound waves (typically around 40 kHz) to detect objects and measure their distance. It operates on the principle of echo ranging—the sensor emits an ultrasonic pulse, which reflects off an object, and the time taken for the echo to return is measured. Using the speed of sound in air, the distance is calculated as:

Distance = Time×Speed of Sound / 2

C.    BREADBOARD

A breadboard is a reusable, solderless prototyping platform widely used in electronics projects, including Arduino-based object detection systems. It allows quick assembly and testing of circuits without permanent connections, making it ideal for research and iterative development.

·         Structure: Consists of a grid of interconnected holes arranged in rows and columns, with internal metal strips providing electrical connectivity.

·         Functionality: Components like resistors, sensors (e.g., ultrasonic), LEDs, and jumper wires can be inserted directly for rapid circuit building.

Advantages:

·         No soldering required — easy to modify or troubleshoot.

·         Supports both digital and analog connections for microcontrollers like Arduino.

·         Cost-effective and reusable for multiple experiments.

Role in Object Detection Projects:

·         Facilitates quick integration of Arduino, ultrasonic sensors, and servo motors.

·         Enables testing of signal flow from sensor to microcontroller before final PCB design.

D.   SERVO MOTOR

A servo motor is a special type of motor used when precise control of angle, position, or rotation is required. It is commonly used in robotics, RC cars, drones, automation machines, and industrial equipment.

Main Parts of a Servo Motor

1.       DC Motor – Provides rotation

2.       Gearbox – Reduces speed and increases torque

3.       Position      Sensor  (Potentiometer/Encoder)           – Measures the angle

4.       Control Circuit – Compares the actual position with the required position and adjusts the motor

How a Servo Works

A servo motor receives a PWM signal (Pulse Width Modulation).

·         Pulse of 1 ms → 0°

·         Pulse of 1.5 ms → 90°

·         Pulse of 2 ms → 180°

E.    BUZZER

A buzzer is a device that produces a buzzing or beeping sound. It is often used as a signaling mechanism in various applications, such as timed tests, sporting events, and alarm systems. Buzzers can be mechanical or electronic, and they come in various shapes and sizes. Some common types of buzzers include piezoelectric buzzers, magnetic buzzers, and speaker buzzers.

Come in various shapes and sizes.

F.    ARDUINO IDE

The Arduino Integrated Development Environment (IDE) is an open-source software platform used to write, compile, and upload code to Arduino microcontroller boards. It plays a crucial role in object detection projects by providing a simple yet powerful interface for programming sensors, actuators, and communication modules.

In the context of object detection using Arduino, the IDE acts as the bridge between hardware (Arduino board + sensors) and software (detection algorithms), enabling rapid prototyping, debugging, and performance optimization.

All data are stored on Arduino-IDE, with the help of this IDE we are integrating Hardware + Software and hence our object detection is working. IDE as in figure 3 contains the whole programming behind this project and this helps us to make our prototype working.

Fig. 3: Real: Source Code (collected through Arduino-IDE).

3. Results and Discussion

The Automatic Toll Gate system was successfully designed, implemented, and experimentally evaluated under different operating conditions to assess its performance, accuracy, and reliability. The integrated hardware–software framework consisting of an RFID reader, microcontroller unit, gate actuation mechanism, and IoT-based monitoring interface operated as expected during real-time testing.

During experimentation, RFID tags mounted on vehicles were detected accurately as the vehicles approached the toll gate. Upon successful identification of a valid RFID tag, the system automatically verified vehicle credentials, processed toll deduction, and actuated the gate opening mechanism without manual intervention. Similar observations have also been reported in earlier RFID-based electronic toll collection systems, where automation significantly reduces processing time and human dependency [1], [2].

Figure 4 is the complete hardware model for the proposed gas monitoring system, consist of gas cylinder base, display for gas measurement, complete hardware below the base and the collected data can also be viewed through mobile phone (via cloud). Figure 4 is the replica of exact hardware model.

Fig.4. Complete hardware model

The response time of the system was found to be minimal, ensuring smooth traffic flow at the toll gate. Compared to conventional manual toll collection, the proposed system reduced average transaction time per vehicle and minimized congestion at the toll plaza. These findings are consistent with existing studies that highlight the efficiency of RFID and IoT-enabled toll systems in improving throughput and reducing delays [3], [4].

Fig.5: Replica of Exact hardware model

The IoT module enabled real-time monitoring of toll transactions and system status, thereby enhancing transparency and operational control. Such real-time data availability is crucial for smart transportation systems and has been emphasized as a key advantage in IoT-based toll management frameworks [5], [6].

The automated system also eliminated cash handling, reducing the chances of human error and revenue leakage. Additionally, the system demonstrated stable operation with low power consumption, making it suitable for continuous deployment. Similar performance benefits have been observed in low-cost automated toll gate prototypes designed for smart highway applications [7].

6. Conclusion

In this paper, we presented the idea of an Automatic Toll Gate System in which we have added features to automatically detect a vehicle via RFID, deduct the toll amount, and continuously monitor the transaction status. It is an efficient, comprehensive solution for highway authorities and commuters, significantly improving home safety.

ACKNOWLEDGMENT

I would like to express my sincere gratitude to our Head of Department, Prof. (Dr.) Pankaj Jha, for his constant support, valuable guidance, and encouragement throughout the duration of this work. I am also thankful to the B.Tech. Final Year students whose active participation, consistent efforts, and cooperation contributed significantly to the successful completion of this project.