Implementation on IoT Based Traffic Management System for Emergency Vehicles

Abstract

In many Indian cities along with those in other nations, congestion caused by traffic is a serious issue. Traffic congestion is caused by signal failure, ineffective law enforcement, and poor traffic administration. The economy, the environment, and general quality of life are all negatively impacted by traffic congestion. Therefore, it is imperative that the traffic congestion issue be managed efficiently. The proposed system\'s goal is to suggest a smart traffic control and management system that makes use of the Internet of Things, a decentralised strategy and algorithms to handle all traffic circumstances more precisely. The shortcomings of the existing traffic control systems will be fixed by the proposed system. To reduce traffic congestion, a forecast of upcoming traffic density will be made using an algorithm. The proposed method enables an emergency vehicle to travel directly to its location by turning all red lights on its route into green ones, thereby reducing traffic congestion. The system manages traffic signals and reduces wait times during emergencies. This makes it an endeavour that can save a life.

Introduction

I. INTRODUCTION

Reduced velocities, extended travel durations, and elongated vehicle queues are among the repercussions of traffic congestion on highways. The occurrence of traffic congestion arises when the number of vehicles on a particular route surpasses its capacity. This is a prominent issue in the primary urban areas of India. Traffic congestion materialises when the demand outstrips the capacity of the highways.

In the contemporary era, rapid mobility has become ubiquitous. This has led to a surge in traffic on highways, often resulting in uncontrollable congestion and high volume. These occurrences are particularly prevalent in major metropolitan areas, forcing numerous individuals to endure extended periods of stagnation in tedious traffic jams. Furthermore, traffic congestion increases the likelihood of road accidents, impeding the response time of emergency vehicles such as paramedics, fire engines, and police cars, thus contributing to the loss of innocent lives. This project is intended for densely populated urban areas with high traffic volumes. In the city of Bangalore, for instance, traffic congestion is a prevalent issue. It is common for traffic to extend over a minimum distance of 100 metres. Under such circumstances, the sound of the ambulance siren may not reach the traffic police officers in a timely manner. Consequently, paramedics are compelled to wait until the traffic clears before proceeding with their emergency response, which could result in adverse outcomes for the patient. The implementation of this project mitigates these challenges.

This project aims to address the challenges posed by traffic congestion in densely populated urban areas with high traffic volumes, such as Bangalore. In such areas, it is common for traffic to extend over a minimum distance of 100 metres, making it difficult for the sound of an ambulance siren to reach the traffic police officers in a timely manner. As a result, paramedics may have to wait for the traffic to clear, potentially endangering the patient's life. However, our system provides a solution by automatically halting the traffic lights and granting the ambulance a green light when it approaches a traffic signal. This is achieved through an IoT-enabled device that monitors and controls traffic signals, reducing traffic congestion and providing emergency vehicles with expedited access through designated green lanes The proposed system involves the installation of Radio Frequency (RF) readers at traffic junctions, which are designed to read the Radio Frequency ID tags on approaching vehicles. Radio Frequency ID technology utilises integrated circuits to store digital data, which is transmitted to Radio Frequency readers via a small antenna embedded within the Radio Frequency ID tag.

II. LITERATURE REVIEW

1. The authors propose an autonomous traffic diversion system that utilises sensors and a computer to modify traffic signals based on vehicle volume, with an emergency override for emergency vehicles. The main aim is to enhance traffic flow and alleviate congestion. MATLAB simulations demonstrate that the suggested approach substantially boosts transportation efficiency and reduces traffic congestion.
2. The proposed solution utilises IoT technologies such as RFID, GPS, and GSM to identify approaching emergency vehicles and prioritise them in the traffic management system. A mobile application is also developed to allow emergency vehicle drivers to interact with the system and receive real-time traffic information. The system's performance was evaluated using Raspberry Pi and Arduino, and the findings demonstrate a significant improvement in emergency vehicle mobility by reducing response times in urban traffic.
3. The researchers have proposed an autonomous traffic light system that prioritises emergency vehicles, resulting in reduced response times and improved survival rates. The system employs sensors on emergency vehicles to communicate with traffic signals, thereby facilitating their passage. Additionally, the authors recommend integrating the system with GPS to calculate the most efficient path for emergency vehicles to reach their destination. Furthermore, the device can notify hospitals of the approaching emergency vehicles.
4. The paper underscores the importance of reducing emergency vehicle response times and highlights the limitations of conventional traffic management systems. The authors examined several systems that employ IoT, RFID, GPS, and wireless communication technologies to prioritise emergency vehicles while ensuring smooth traffic flow. The report evaluates the strengths and weaknesses of each approach and proposes potential areas for future research in this field.
5. The proposed method aims to alleviate traffic congestion by adjusting traffic signal timings based on traffic density. Using an IoT platform, the system communicates with traffic signal management, and the controller modifies the signal timings accordingly. The research provides empirical evidence that the proposed approach improves traffic flow and reduces congestion. However, since the study primarily focuses on visual processing for traffic density assessment, emergency vehicle recognition and priority are not incorporated into the method.
6. The article proposes a sophisticated traffic control system to address the issue of traffic congestion. The system employs sensors, cameras, and IoT devices to collect traffic flow and road condition data, which is analysed in real-time to regulate traffic flow and dynamically adjust traffic signal timing. Furthermore, a mobile application is provided for users to obtain real-time traffic updates and modify their routes accordingly. The expected outcomes of the system include a reduction in traffic congestion, shorter commute times, and improved fuel efficiency.
7. In order to enhance the effectiveness and safety of vehicle traffic, a smart traffic management system based on IoT is being developed, consisting of sensors, a microprocessor, and a web-based program. The sensors gather data on traffic density, which is then processed by the microcontroller to determine the timing of traffic signals. Internet application users can obtain real-time traffic information through the system. The system's primary objective is to improve traffic flow by decreasing wait times at traffic signals and reducing accidents.
8. The proposed system aims to mitigate congestion and enhance traffic flow by utilising real-time traffic data from sensors and cameras. It includes a sensor network, cloud-based data processing and analysis, and an end-user mobile application. Machine learning algorithms are employed to predict traffic patterns and adjust traffic signals accordingly. The authors contend that their technology has the potential to reduce travel time and improve traffic flow, making it a promising solution for smart cities.
9. The system prioritises emergency vehicles by utilising RF sensors to detect them and activate the traffic light control system. A limited-scale implementation and testing of the system demonstrated its effectiveness in managing traffic for emergency vehicles. However, the research did not address the system's ability to scale to larger regions, which could pose a drawback.
10. The article suggests a density-based smart traffic system that utilises IoT for real-time data processing to alleviate traffic congestion. Ultrasonic sensors are employed to detect vehicle density on the road, and the data is transmitted to a central processing unit (CPU) for further analysis. A decision-making process is used by the CPU to determine the optimal traffic light timings and synchronises them with nearby signals. Furthermore, a web application is provided to offer users real-time traffic statistics.

III. LITERATURE TABLE

IV. PROPOSED SYSTEM

Here, we describe a ground-breaking IoT-based method for automated traffic signal monitoring that fully automates the operation of the traffic light system. The proposed system operates normally in normal traffic, but if the volume of traffic reaches a certain threshold, it can efficiently control the density of traffic signals by using an Arduino-based circuit system that uses infrared (IR) sensors to detect the volume of traffic on a particular lane. The effective management of traffic conditions on the highways is made possible by this sophisticated traffic control system, which shows the present traffic density.When emergency vehicles or other high-priority vehicles pass by, the suggested system may also change the timings of the traffic lights in real-time, making them green while maintaining the other signals red.RFID tags and a receiver work together to enable this capability.To maintain smooth traffic flow, the system uses a powerful control algorithm that can analyse the traffic density data gathered by the IR sensors and modify the traffic lights as necessary. The system connects to other city traffic light systems using IoT technology, providing effective traffic control over the whole road network.

Modern traffic control systems now frequently include the usage of RFID tags on emergency vehicles. These tags can be attached to the cars directly or integrated into already installed communication devices like radios or GPS systems. When an emergency vehicle with an RFID tag approaches a traffic light, the system instantly recognises the tag and may change the traffic signal to allow the vehicle to safely and quickly pass through the junction.

It is obvious how useful RFID technology is in emergency circumstances. The technology can assist to minimise delays in emergency response times, potentially saving lives by lowering the length of time that emergency vehicles must wait at traffic signals. Because emergency vehicles are given preference over other cars in traffic flow, the adoption of RFID tags can also serve to lower the probability of accidents involving these vehicles.

The application of RFID technology in traffic management systems can help the general flow of traffic in addition as reducing the response times for emergency vehicles. RFID technology can assist to enhance traffic flow and shorten travel times for all cars on the road by decreasing congestion and delays brought on by emergency circumstances.

VII. ACKNOWLEDGEMENT

We would like to offer our heartfelt appreciation to everyone who helped and supported us during this effort. We would like to express our heartfelt gratitude to Dr. Kamalakshi Naganna, Professor and Head, Department of Computer Science and Engineering, Sapthagiri College of Engineering, and our project guide Ms. Hemalatha K, Assistant Professor, Department of Computer Science and Engineering, Sapthagiri College of Engineering, who have provided constant guidance, support, and encouragement throughout the project. Their excellent views and comments have been important in designing our project. Finally, we are grateful to our friends and family for their unwavering support and encouragement.

Conclusion

In conclusion, this project represents a major effort to create a more effective and efficient traffic management system. The project has focused on several key objectives, including reducing congestion and improving safety, reducing emergency vehicle response times, saving time at traffic junctions, reducing pollution, enabling timely organ transportation, and providing real-time adjustments to traffic light patterns. By incorporating these features, the system has the potential to improve traffic flow, enhance road safety, and reduce pollution levels, ultimately resulting in a better quality of life for citizens. The project is a significant step forward in the development of intelligent traffic management systems, with the potential to make a substantial impact on traffic management in urban areas. The outcomes of this project will contribute towards making the roads safer and more efficient, ensuring that traffic moves smoothly, reducing travel times, and improving overall traffic flow.

References

[1] K. S. Sandhya & B. Karthikeyan; Automatic Traffic Diversion System Using Traffic Signals ; International Conference on Nextgen Electronic Technologies , 978-1-5090-5913-3/17/ ©2017 IEEE [2] Shubhankar Vishwas Bhate, Prasad Vilas Kulkarni, Shubham Dhanaji Lagad & Mahesh Dnyaneshwar Shinde; IoT based Intelligent Traffic Signal System for Emergency vehicles ; Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT 2018) [3] A.S.Dhatrak & Dr.S.T.Gandhe; Automatic Traffic Signals in Smart Cities for Speedy Clearance of Emergency Vehicles; 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) 978-1-5386-1974-2/18/©2018 IEEE [4] Diksha A. Chaudhari, Lakxmi S. Gadhari, Gaurav H. Damale & Abhijit R. Dutonde; A Survey on Traffic Control Mechanism for Emergency Vehicles; International Journal of Engineering Research & Technology (IJERT) http://www.ijert.org ISSN: 2278-0181 IJERTV9IS060832 (This work is licensed under a Creative Commons Attribution 4.0 International License.) Published by : www.ijert.org Vol. 9 Issue 06, June-2020 [5] Anilloy Frank, Yasser Salim Khamis Al Aamri & Yasser Salim Khamis Al Aamri; IoT based smart traffic density control using image processing ; 978-1-5386-8046-9/19/$31.00 ©2019 IEEE [6] Sabeen Javaid, Ali Sufian, Saima Pervaiz & Mehak Tanveer; Smart Traffic Management System Using Internet of Things, 2018 [7] Rachana K P , Aravind R, Ranjitha M, Spoorthi Jwanita & Soumya K; IoT Based Smart traffic Management System , Published by, www.ijert.org, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Published by, www.ijert.org NCCDS - 2021 Conference Proceedings [8] Syed Misbahuddin, Junaid Ahmed Zubairi, Abdulrahman Saggaf, Jihad Basuni, Sulaiman A-Wadany and Ahmed Al-Sofi, IoT Based Dynamic Road Traffic Management For Smart Cities , 978-1-4673-9268-6/15/ ©2015 IEEE [9] Goshwe, Y. Nentawe, Okewu A. Victor and Kureve D.Teryima; Radio Frequency Sensor-Based Traffic Light Control For Emergency Vehicle ; International journal of scientific & technology research volume 8, issue 08, august 2019 [10] Naga Harsha.J, Nikhil Nair , Sheena Mariam Jacob and J. John Paul; Density Based Smart Traffic System with Real Time Data Analysis Using IoT ; Proceedings of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India.

Copyright

Copyright © 2023 Mayank Raj, Anubhaw Anand, Priyesh Raj, Shejal Singh, Ms. Hemalatha K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.