Real-Time Traffic Light Optimization Using AI and IOT

Abstract

Urban traffic congestion is a significant challenge globally, impacting transportation efficiency, environmental sustainability, and urban liveability. Traditional traffic control systems often struggle to adapt to changing traffic dynamics, leading to increased congestion and delays. This paper presents a novel traffic management system developed by our team, leveraging cutting-edge technologies such as computer vision, artificial intelligence (AI), and the Internet of Things (IoT). Deployed at intersections, our system utilizes real-time CCTV feeds for traffic analysis, employing advanced image processing and machine learning algorithms, including YOLO V7, to dynamically assess traffic density. These insights inform adaptive adjustments to traffic light timings, with the aim of reducing congestion, enhancing transit efficiency, and mitigating pollution. Our signal switching algorithm, incorporating dynamic logic developed by our team, iteratively adjusts signal timings based on real-time traffic conditions, ensuring responsive and efficient traffic flow management. Through the integration of our innovative technologies, our system seeks to revolutionize urban transportation networks, promoting smarter, more adaptive, and sustainable urban environments.

Introduction

I. INTRODUCTION

In the ever-growing cities, the rise in the number of vehicles has caused problems on roads, leading to reduced capacity and a drop in service quality. One critical issue contributing to this is the old-fashioned traffic control systems at intersections, using fixed signal timers. These systems repeat the same patterns without adapting to the changing traffic conditions, causing more traffic and inefficiency. To tackle these challenges, this project explores a new way of controlling traffic. It discusses the drawbacks of manual control, fixed-timer traffic lights, and electronic sensors, paving the way for an innovative solution using Computer Vision. By using live CCTV images at intersections, this system aims to figure out real-time traffic density, categorize vehicles, and adjust signal timings dynamically. This approach promises a more responsive, efficient, and eco-friendly traffic management system. Develop an intelligent traffic light management system using Computer Vision, Artificial Intelligence (AI), and Internet of Things (IoT) to achieve real-time traffic density calculation, vehicle classification, and adaptive signal timing. The primary goal is to reduce congestion, enhance traffic flow, and minimize environmental impact for a more efficient and sustainable urban transportation The project aims to ease congestion at intersections, reducing delays and pollution caused by the increasing number of vehicles in urban areas. Motivated by the limitations of current traffic control systems, it seeks to introduce an intelligent, responsive system using advanced technologies like Computer Vision and AI. This approach, beyond conventional methods, adapts traffic signals dynamically based on real-time conditions. With case studies from cities like Mumbai and Bangalore emphasizing the need for innovation, the project aims to enhance traffic control effectiveness, reduce travel times, and contribute to a sustainable urban transportation system. By integrating IoT devices, the system will gather real-time data on traffic flow, enabling precise adjustments to optimize signal timings further. Additionally, the project will explore AI algorithms to predict traffic patterns, facilitating proactive measures for congestion management. Through continuous monitoring and adaptation, the proposed system aims to revolutionize urban traffic management, offering a scalable and sustainable solution for future cities.

II. BACKGROUND

We get motivated by disadvantages of the existing system. The purpose of the system is to decrease congestion at intersections primarily, and reduce delays and pollution. The motivation to commence on this project arises from the critical issues linked to the surging number of vehicles in urban areas, leading to worsening traffic congestion. This congestion not only causes delays and stress for drivers but also contributes to higher fuel consumption and increased air pollution.

The negative impacts are particularly evident in megacities, underscoring the urgency of addressing the challenges of traffic management in real-time. The current traffic control systems, often reliant on fixed signal timers and outdated methods, prove insufficient in adapting to the dynamic nature of urban traffic. This project is motivated by the need to revolutionize traffic management and introduce a more intelligent and responsive system that can mitigate congestion, improve transit efficiency, and contribute to a more sustainable and eco-friendlier urban environment. Cities like Mumbai and Bangalore serve as impactful case studies, highlighting the urgency of finding innovative solutions. The excessively long travel times during rush hours in these cities underscore the inadequacies of traditional traffic control methods. The motivation is to create a system that goes beyond conventional approaches, leveraging a suite of cutting-edge technologies such as Computer Vision, Artificial Intelligence (AI), and Internet of Things (IoT) to dynamically adjust traffic signals based on real-time conditions.

III. LITERATURE REVIEW

1. Paper A : Smart Control of Traffic Light Using Artificial Intelligence:

This paper addresses the escalating issue of traffic congestion in cities, exacerbated by population growth and increased automobile usage. Traffic jams not only cause delays and stress for drivers but also lead to higher fuel consumption and air pollution, particularly affecting megacities. Real-time calculation of road traffic density is crucial for effective signal control and traffic management. The proposed system utilizes live images from traffic junction cameras for traffic density calculation through image processing and AI. Additionally, it presents an algorithm for adaptive traffic light control based on vehicle density to mitigate congestion, ultimately aiming to provide faster transit and reduce pollution.[2]

Limitations:

• Reliance on live camera feeds may introduce delays and inaccuracies due to environmental factors.

Continuous calibration is required for optimal performance, which may be resource-intensive, time-consuming.

2. Paper B : Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms:

The paper focuses on predicting traffic flow at intersections using machine learning and deep learning algorithms to facilitate adaptive traffic control. Utilizing public datasets, ML and DL models are trained and validated for traffic flow prediction, laying the groundwork for smart traffic light controllers. While the proposed models show promising performance, limitations may include the need for extensive data preprocessing and model training as well as potential challenges in real world deployment due to variations in traffic patterns and environmental factors.[4] .

Limitations:

• Extensive data preprocessing and model training are needed, which may require significant computational resources.
• Real world deployment may face challenges due to variations in traffic patterns and environmental conditions.

3. Paper C : Design and Development of Portable Smart Traffic Signalling System with Cloud Artificial Intelligence  Enablement:

The paper introduces a cost-effective, IoT-enabled portable traffic signalling system designed for smaller traffic junctions. Utilizing ESP32 microcontrollers and foldable mechanical structures, the system offers manual and automatic traffic control modes, with the ability to gather traffic density information and optimize signal timings based on cloud-based algorithms. While the system demonstrates robustness and accuracy in traffic management, limitations may include scalability issues for larger intersections and dependence on constable internet connectivity for cloud-based operations.[1] .

Limitations:

• Scalability Issues May Arise for larger intersections, limiting the system's applicability.
• Dependence Unstable Internet Connectivity for Cloud-based operations may hinder functionality in areas with poor                      connectivity.

4. Paper D : Real-Time Traffic Light Optimization Using Simulation of Urban Mobility:

The paper presents a study on real-time traffic light optimization using the SUMO tool for simulating urban mobility. Testing Scenarios in Berlin city, the study analyses traffic performance under different scenarios, optimizing traffic flow by modifying various parameters such as traffic lights and road construction. While the study demonstrates the potential for optimizing traffic performance, limitations may include the complexity of accurately modelling real-world traffic behaviour and the challenge of extrapolating simulation results to practical implementations diverse urban environments.[5]

Limitations:

• Accurately modelling real-world traffic behaviour simulation may be complex and challenging.
• Extrapolating simulation results to practical implementations in diverse environments may not fully capture real world complexities.

IV. COMPARISON OF ALGORITHMS EFFECTIVENESS

V. FUTURE DIRECTIONS

Future research directions include further optimization of AI algorithms for traffic prediction and control, integration of IoT technologies for enhanced data collection, and collaboration with city authorities for real-world implementation and testing. Additionally, exploring hybrid approaches that combine the strengths of different algorithms could yield more adaptable and efficient solutions. Moreover, integrating emerging technologies like edge computing and 5G connectivity could further enhance real-time data processing and decision-making capabilities for smarter urban mobility systems.

Conclusion

The proposed intelligent traffic management system presents a promising solution to alleviate urban traffic congestion and enhance transit efficiency. Through the utilization of computer vision and AI technologies, the system endeavours to overcome the shortcomings of conventional traffic control systems, fostering the creation of a smarter and more efficient urban transportation network. Nonetheless, challenges pertaining to data accuracy and scalability persist, underscoring the necessity for continuous research and development in this domain. Moreover, the system\'s ability to minimize unnecessary stops and starts not only improves traffic flow but also results in substantial fuel savings and emissions reduction, thereby offering a positive environmental impact. Moving forward, further advancements in algorithm optimization, real-time data processing, and seamless integration with existing infrastructure are imperative to realize the full potential of intelligent traffic management systems in urban environments.

References

[1] Design and development of portable smart traffic signalling system with cloud-artificial intelligence enablement . Indonesian Journal of Electrical Engineering and Computer Science Vol. 26, No. 1, April 2022, pp. 116~126 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v26.i1.pp116-126 . Badarinath Kamisetty 1 , Mahesh Renduchintala1 , Lochan Lingaraja Shetty2 , Suresh Chandarshekar2 , Rajashree Shettar11 . [2] Smart Control of Traffic Light Using Artificial Intelligence. 78-1-7281-8867-6/20/$31.00 ©2020 IEEEDOI:10.1109/ ICRAIE 51050.2020. 9358334. Mihir M. Gandhi, Devansh S. Solanki, Rutwij S. Daptardar, Nirmala Shinde Baloorkar. [3] Lilhore, U.K.; Imoize, A.L.; Li, C.-T.; Simaiya, S.; Pani, S.K.; Goyal, N.; Kumar, A.; Lee, C.-C. Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities. Sensors 2022, 22, 2908. https://doi.org/10.3390/s22082908 [4] Navarro-Espinoza, A.; López-Bonilla, O.R.; García-Guerrero, E.E.; Tlelo-Cuautle, E.; López-Mancilla, D.; Hernández-Mejía, C.; Inzunza-González, E. Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms. Technologies 2022, 10, 5. https://doi.org/10.3390/technologies10010005. [5] .Real Time Traffic Light Optimization Using Simulation of Urban Mobility Tanya Garg1·Gurjinder Kaur· Prashant Singh Rana2SN Computer Science (2023) 4:526 https://doi.org/10.1007/s42979-023-01916-9 . [6] ‘Pygame Library’, 2019. [Online]. Available: https://www.pygame.org/wiki/about.

Copyright

Copyright © 2024 Aniket Phand, Shweta Bagade, Nikhil Bandgar, Prof. Ganesh Wayal. 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.