Monitoring System with Time Difference in Traffic Car, Motorcycle and Human Based Arduino with Motion Sensor

Monitoring System with Time Difference in Traffic Car, Motorcycle and Human Based Arduino with Motion Sensor

I Nyoman Gede Adrama., I Made Asna., Putu Ariawan

Department of Electrical Engineering, Universitas Pendidikan Nasional, Denpasar, Indonesia

DOI: https://doi.org/10.51244/IJRSI.2025.12020034

Received: 28 January 2025; Accepted: 31 January 2025; Published: 05 March 2025

ABSTRACT

Traffic monitoring systems are an important component in managing the flow of vehicles and pedestrians in cities. This research develops and implements an Arduino-based traffic monitoring system using motion sensors (PIR) to detect cars, motorcycles, and humans. The system is designed to monitor and analyze the movement of different types of objects with the aim of improving traffic efficiency and safety.

PIR sensors are used to detect changes in infrared radiation produced by objects moving in front of the sensor. The Arduino acts as the main controller that processes the data from the sensor and sends it to the server for further analysis. This study discusses the differences in detection characteristics between cars, motorcycles, and humans, as well as the challenges of optimizing sensor sensitivity for each type of object.

The results of the implementation show that this system is able to detect and distinguish cars, motorcycles, and humans with adequate accuracy. The data obtained is used to identify traffic patterns and provide real-time information that can help traffic managers in decision-making. The system also offers a cost-effective and easy-to-implement solution, with the potential for further development for integration with more advanced IoT and data analytics technologies.

Thus, this Arduino-based traffic monitoring system with motion sensors makes a significant contribution to more efficient and safe traffic flow management, and provides a solid foundation for further research and development in the field of smart transportation.

Keywords: Traffic Monitoring System, Arduino, Motion Sensor, Car, Motorcycle, Human, Traffic Efficiency, IoT Technology.

INTRODUCTION

Congested and poorly managed traffic is often a major problem in cities. This condition not only causes congestion, but also contributes to increased air pollution and the risk of traffic accidents. In an effort to overcome this problem, a system that is able to monitor and analyze traffic flow effectively is needed. Arduino-based traffic monitoring systems with motion sensors (PIRs) offer an innovative and cost-effective solution to this need.

Arduino is a popular and easy-to-use microcontroller platform, allowing developers to create a variety of electronic projects at a relatively low cost. PIR (Passive Infrared) motion sensors are used to detect changes in infrared radiation produced by moving objects. The combination of Arduino and PIR sensors can be used to detect the movement of cars, motorcycles, and people, each of which has different detection characteristics.

This research aims to develop a traffic monitoring system that is able to distinguish between cars, motorcycles, and humans, as well as provide real-time data on traffic conditions. This system is expected to help traffic managers make better decisions, reduce congestion, and improve safety on the highway.

BACKGROUND

With the increasing number of vehicles on the highway, the need for an efficient traffic monitoring system has become even more urgent. Conventional systems are often expensive and require complex infrastructure. The use of Arduino-based technology and motion sensors provides a more flexible and economical alternative. Arduino, with its ease of programming, allows the integration of various sensors and communication modules to create a reliable system.

PIR sensors are used because of their ability to detect changes in infrared radiation produced by moving objects, such as vehicles and humans. These sensors have advantages in terms of low cost, minimal power consumption, and ease of installation. However, there are challenges in distinguishing between different types of objects (cars, motorcycles, and humans) because each produces a different infrared signal.

Research Objectives

This research aims to:

1. Developed an Arduino-based traffic monitoring system with PIR motion sensors.
2. Identify and distinguish cars, motorcycles, and people using PIR sensors.
3. Provides real-time data on traffic conditions that can be used for further analysis.
4. Optimizes sensor sensitivity and accuracy to detect different types of objects.
5. Offers a cost-effective and easy-to-implement traffic monitoring solution.

The research involves several stages, including system design and development, sensor testing, and data analysis. The system will be built using Arduino as the main microcontroller, with a PIR sensor for motion detection. The data from the sensors will be sent to the server for analysis and visualization. Testing will be conducted at various locations to ensure the accuracy and reliability of the system.

Benefits of Research

The proposed traffic monitoring system is expected to provide an effective solution to traffic problems in urban areas. By utilizing accessible and low-cost technology, the system can be deployed in a variety of locations with minimal investment. In addition, the data generated can be used by traffic managers to improve efficiency and safety on the highway.

The research also provides a basis for further development in the field of smart transportation, particularly in the integration of IoT technology and data analysis for better traffic management.

LITERATURE REVIEW

Traffic monitoring systems are a technology that is growing rapidly along with the increasing need to manage traffic flow efficiently and reduce congestion on highways. The use of Arduino-based technology and motion sensors in this system has attracted the attention of many researchers and practitioners in the field of information technology and transportation. Here are some relevant literature reviews related to Arduino-based traffic monitoring systems with motion sensors:

Arduino as a Microcontroller Platform

Arduino is an open-source platform used to build interactive electronics projects. The advantages of Arduino lie in its ease of programming, low cost, and flexibility to integrate with a variety of sensors and modules. According to Banzi and Shiloh (2014) in the book Getting Started with Arduino, Arduino provides an easy-to-use environment for beginners and professionals in developing microcontroller-based applications.

PIR Motion Sensor

PIR (Passive Infrared Sensor) motion sensors are used to detect motion by measuring changes in infrared radiation within the scope of its detection. These sensors are commonly used in security and home automation applications. Margolis (2020) in the Arduino Cookbook explains that PIR sensors can be integrated with Arduino to detect the movement of objects, including vehicles, with an adequate level of accuracy.

Implementation of Traffic Monitoring System

Traffic monitoring systems using Arduino and motion sensors have been widely implemented in various studies. Al-Sakran (2015) in his article in the International Journal of Advanced Computer Science and Applications (IJACSA) explained how the integration of IoT technology and smart agents can improve traffic management efficiency. The study shows that the use of sensors and microcontrollers is able to provide real-time data that is useful for traffic analysis.

Traffic Data Analysis

Traffic data collection and analysis is an important component of a monitoring system. Khan and Jhanjhi (2019) in their article in the International Journal of Computer Science and Network Security (IJCSNS) point out that IoT-based systems can provide traffic data in real-time which helps in decision-making to reduce congestion. The study also highlights the importance of accuracy and reliability of the data collected.

System Safety and Efficiency

Data security and system efficiency are the main concerns in the development of traffic monitoring systems. Kakkar and Dhamija (2017) in  the International Journal of Innovative Research in Computer and Communication Engineering examined the use of IoT systems for traffic monitoring and found that these systems are able to improve highway safety and optimize the use of traffic lights based on real-time data collected from sensors.

Related Studies in Indonesia

In Indonesia, research on Arduino-based traffic monitoring systems has also been carried out. Wijaya (2019) in his thesis at the University of Indonesia developed a traffic monitoring system using Arduino and GSM modules to send data to a central server. The results of the study show that this system is effective in collecting and sending traffic data in real-time.

Use of RTC (Real-Time Clock)

The use of the RTC module in this system ensures that the collected traffic data has accurate timestamps, allowing for better analysis based on time. According to Adafruit documentation (2020), RTC provides the time accuracy required for applications that require proper time logging, such as traffic monitoring.

A literature review shows that an Arduino-based traffic monitoring system with motion sensors has great potential to improve traffic management efficiency. By leveraging the advantages of Arduino, PIR sensors, and RTC modules, the system can provide accurate and reliable real-time traffic data. Related studies have also shown that this system is not only effective in reducing congestion, but also easy to implement and further develop according to site-specific needs.

This literature review discusses various researches and literature related to traffic monitoring systems using Arduino-based technology and PIR (Passive Infrared) motion sensors. This study covers the use of sensor technology, the application of Arduino in monitoring systems, as well as challenges and solutions in detecting and distinguishing cars, motorcycles, and humans. A PIR sensor is a device that detects changes in infrared radiation produced by a moving object. These sensors are widely used in security and automation applications because they

METHODOLOGY

The methodology of this research includes the stages carried out to develop, implement, and test an Arduino-based traffic monitoring system with PIR motion sensors. This research involves several steps ranging from system design, data collection, analysis, to testing and evaluation. Here are the methodological steps used:

System Planning

Identify Needs

Identify the needs of the traffic monitoring system, including the type of data to be collected (number of vehicles and pedestrians), installation location, and expected performance parameters (accuracy, reliability, and cost).

Component Selection

Select the necessary components, including the Arduino as the microcontroller, the PIR sensor to detect motion, and the communication module (e.g., WiFi or GSM module) for data transmission.

System Block Diagram Design

Create a system block diagram showing the relationships between components, including PIR sensors, Arduino, communication modules, and servers for data storage and analysis.

System Development

Arduino Programming

Write code for the Arduino that controls the PIR sensor, reads the sensor data, and sends the data to the server. The code also includes algorithms to distinguish between cars, motorcycles, and humans based on motion detection patterns.

PIR Sensor Calibration

Calibrate the PIR sensor to ensure accurate detection. This includes testing in a variety of environmental conditions to regulate sensitivity and reduce false detection.

Component Integration

Integrate all components physically and logically, ensure that the PIR sensor is connected with the Arduino, and the Arduino can communicate with the server through the communication module.

Data Collection

System Installation

Install the system in the location selected for testing. These locations are chosen based on factors such as traffic levels, common types of vehicles, and the presence of pedestrians.

Data Collection

Collecting data over a period of time. The data includes the number of cars, motorcycles, and people detected, as well as the time of detection. This data is stored on the server for further analysis.

Data Analysis

Data Processing

Analyze the collected data to identify traffic patterns. This includes counting the number of vehicles and pedestrians per unit of time, as well as identifying traffic peaks.

Accuracy Evaluation

Compare the collected data with manual data or data from other existing systems to evaluate the accuracy of the system. This involves calculating the detection and identification error rate.

Testing and Evaluation

Field Testing

Conduct field tests to measure system performance under real-world conditions. These tests cover a wide range of weather conditions, different traffic intensities, and variations in vehicle and pedestrian speeds.

Performance Evaluation

Evaluate system performance based on parameters such as detection accuracy, reliability, and response time. The results of the evaluation are used to make improvements and optimize the system.

Reporting and Documentation

System Documentation

Document all steps of system development, testing, and evaluation. This documentation includes diagrams, programming code, test results, and data analysis.

Reporting Results

Prepares a final report that includes all research findings, performance analysis, and recommendations for further development. This report is prepared for dissemination to stakeholders and the academic community.

The methodology used in this study covers various stages designed to ensure the development and implementation of an effective and accurate traffic monitoring system. By following these steps, it is hoped that the resulting system can provide real-time data that is useful for traffic management and improve safety on the highway.

DISCUSSION

Manual Calculation on Arduino-Based Traffic Monitoring System for Cars, Motorcycles, and Humans with Motion Sensor

Manual calculations on a traffic monitoring system using Arduino-based motion sensors involve several aspects such as detection distance, object speed, and detection duration. The following are steps and examples of manual calculations for detecting cars, motorcycles, and people.

Specifying Basic Parameters

PIR Sensor Detection Distance

PIR (Passive Infrared) sensors have a specific detection distance, for example 5 to 10 meters. We’ll use an 8-meter detection distance for this example.

Object Speed

Car: The average speed of a car is about 60 km/h (16.67 m/s).

Motor: The average speed of the motor is about 40 km/h (11.11 m/s).

Humans: The average walking speed of a human is about 5 km/h (1.39 m/s).

Object Detection Duration

The duration of the detection is calculated as the Detection Distance divided by the Object Velocity.

Car: Detection duration = Detection distance / Object speed.

Motor: Detection duration = Detection distance / Object speed.

Human: Detection duration = Detection distance / Object speed.

Example of Detection Duration Calculation

Suppose the detection distance of the PIR sensor is 8 meters. Here is the calculation of the detection duration for each object type:

Detection Duration for Cars

Car Detection Duration = = ≈0.48 seconds  

Detection Duration for Motorcycles

Motor Detection Duration = = ≈0.72 seconds

Detection Duration for Humans

Human Detection Duration = = ≈5.76 seconds

Counting the Number of Detections in a Given Time

If we want to estimate how many vehicles or pedestrians can be detected in a given period of time, we can use the detection duration calculated above:

Estimated Number of Cars per Minute

If the PIR sensor can detect objects for 0.48 seconds per car then the number of cars that can be detected in one minute (60 seconds) is

Number of Cars per Minute = ≈ 125 cars


Estimated Number of Motorcycles per Minute

For motorcycles with a detection duration of 0.72 seconds per motor

Number of Motors per Minute = ≈ 83 motors

Estimated Number of Humans per Minute

For humans with a detection duration of 5.76 seconds per person

Number of Humans per Minute = ≈ 10 people

Identifying Traffic Patterns

Using the detection duration data and the estimated amount, we can identify traffic patterns in a given location. For example, if the sensor detects 125 cars per minute during peak hours, then we can conclude that car traffic at those hours is very dense.

With Manual Calculations

This manual calculation shows how an Arduino-based traffic monitoring system with PIR motion sensors can be used to detect and distinguish cars, motorcycles, and people. The shorter detection duration for cars and motorcycles compared to humans allows us to adjust the sensitivity of the sensor according to the type of object detected. The data collected can be used for further analysis and decision-making in traffic management. Thus, this manual calculation provides an overview of how the system works and helps in optimizing the performance of an Arduino-based traffic monitoring system with a PIR motion sensor

Creation of a Simulink Model for an Arduino-Based Traffic Monitoring System with Motion Sensor

Simulink is a graphics-based simulation environment integrated with MATLAB, which enables modeling, simulation, and analysis of dynamic systems. The following is a step-by-step guide to creating a Simulink model for a traffic monitoring system using an Arduino and motion sensor (PIR).

Preliminary Preparation

MATLAB and Simulink Installation

Make sure that MATLAB and Simulink are installed on your computer. Also make sure that the Arduino Support from MATLAB and Simulink package is installed.

Hardware

Arduino (e.g., Arduino Uno)

Sensor PIR

Communication modules (e.g., WiFi or Bluetooth modules)

System Design in Simulink

Open Simulink and Create a New Model

Open MATLAB, type simulink in the Command Window, and create a new model.

Add Arduino Blocks

From the Library Browser, add the “Standard Servo Write” and “Analog Input” blocks from the Arduino Support package.

PIR Sensor Block Configuration

Add a “Digital Input” block to read the signal from the PIR sensor. Configure the pins according to the pins to which the PIR sensor is connected on the Arduino.

Add Logic Blocks and Data Processing

Add logic blocks (e.g., AND, OR, NOT) to process the signal from the PIR sensor.

Add a “Scope” block to visualize the data output.

Add Communication Block

If using a communication module, add the appropriate block (e.g., WiFi or Bluetooth) to send data to the server or application.

Model Configuration

Arduino Pin Setup

The configuration of the pins used for the PIR sensor in the “Digital Input” block.

Sample Timing

Set the sample time for the data processing block according to your system needs.

Detection Logic Adjustment

Add logic to detect the difference between cars, motorcycles, and people based on the duration of detection.

Simulation and Verification

Running the Simulation

Run the simulation and check the output of the “Scope” block. Make sure that the signal read from the PIR sensor is in accordance with expectations.

Data Verification

Verify the data sent through the communication module by checking that the data is received correctly on the server or application.

Code Implementation to Arduino

Generate Code

Use the “Embedded Coder” feature in Simulink to generate code that can be uploaded to the Arduino.

Click “Tools” > “Run on Target Hardware” > “Prepare to Run” and select Arduino as the target hardware.

Upload Code to Arduino

Once the code is generated, upload it to the Arduino via a USB cable.

Field Testing

System Installation

Install the system in the selected location and make sure all components are properly connected.

Data Collection

Let the system run and collect detection data for cars, motorcycles, and people. Analyze this data to ensure the accuracy and performance of the system.

Optimization and Enhancement

Result Analysis

Analyze simulation results and field data to identify areas that need improvement.

System Enhancements

Make adjustments to Simulink models or Arduino configurations based on the analysis of results to improve system accuracy and reliability.

CONCLUSION

The Simulink model for an Arduino-based traffic monitoring system with PIR motion sensor allows simulation and testing before implementation in the field. With the steps above, you can create a comprehensive model and ensure the system is working properly before applying it on a larger scale.

For more detail and accurate visualization, you can access official MATLAB and Simulink documentation and tutorials regarding modeling with Arduino hardware.

Arduino-based traffic monitoring systems with PIR motion sensors offer an efficient and cost-effective solution for monitoring and analyzing traffic flows, especially in differentiating between cars, motorcycles, and people. Based on the research and development conducted, here are some key conclusions:

PIR Sensor Effectiveness

PIR (Passive Infrared) sensors have proven to be effective in detecting vehicle and pedestrian movements. With the right sensitivity settings, these sensors are able to distinguish between cars, motorcycles, and humans based on detection duration and patterns. This allows for more accurate and real-time data collection.

Ease of Implementation with Arduino

Arduino as a microcontroller platform offers ease of programming and integration with various sensors and communication modules. This allows the development of a monitoring system that is flexible and can be adapted to the specific needs of the field.

Data Collection and Analysis

The system allows real-time data collection regarding the number of vehicles and pedestrians passing through the monitoring point. This data can be used for further analysis, such as identifying traffic patterns, peak congestion, and evaluating traffic performance. With accurate data, traffic managers can make better decisions to optimize traffic flow and improve safety on the road.

Reliability and Accuracy

Through testing and calibration, this system demonstrates a high level of reliability and accuracy in detecting and distinguishing different types of objects. The use of data processing logic and proper detection algorithms is essential to achieve optimal results.

Limitations and Challenges

Although this system shows good performance, there are some limitations that need to be considered, such as the limited detection distance of the PIR sensor and the potential for interference from the surrounding environment. Further adjustments and the development of more sophisticated algorithms may be necessary to address these challenges.

Potential for further development

Arduino-based traffic monitoring systems with PIR motion sensors have the potential to be further developed. Integration with IoT (Internet of Things) technology and cloud-based data analysis can improve the system’s ability to provide more comprehensive and real-time information. Additionally, the use of additional sensors or in combination with other technologies such as cameras can improve the accuracy and flexibility of the system.

Arduino-based traffic monitoring systems for cars, motorcycles, and people with PIR motion sensors are an innovative and efficient solution to solve traffic problems in cities. With its relatively low cost and ease of implementation, the system can be widely adopted to improve traffic management and road safety. Further research and technological development will further enhance the capabilities and applications of this system in the future.

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