A Comparative Study on IoT Based Traffic Management System

Abstract

The continuous growth of the global population has led to a rapid increase in vehicle production, resulting in an exponential growth of vehicles on the roads. This surge contributes to severe traffic congestion, particularly during peak rush hours in metropolitan cities. Urban planners, city officials, and researchers are increasingly concerned about effective traffic management to ensure safety and economic efficiency. Addressing this issue, Intelligent Transportation Systems (ITS) integrate existing technology with current infrastructure to alleviate congestion. This paper focuses on a comparative study of various traffic control methods, specifically the Traffic Light System (TLS), which includes Radio Frequency Identification (RFID), dynamic and static TLS, and the Internet of Things (IoT).

Introduction

I. INTRODUCTION

Traffic congestion poses a significant challenge for urban planners, impacting fuel consumption, air pollution, and overall economic growth. With population growth, congestion is expected to worsen, necessitating a well-organized and adaptable traffic management system. Recent advancements in Radio Frequency Identification (RFID) technology have revolutionized traffic control, offering low-cost and user-friendly solutions. RFID is particularly effective in automatic vehicle identification, reducing the need for expensive infrastructure.

The Traffic Management System (TMS) plays a crucial role in reducing urban traffic congestion. Traditional traffic lights, invented by Lester Farnsworth Wire in 1912, have evolved into dynamic TLS, offering the flexibility to adapt to varying traffic conditions. The integration of advanced technologies, including image processing, wireless communication, data mining, and control systems, has given rise to Intelligent Transportation Systems (ITS). The emergence of the Internet of Things (IoT) further enhances communication capabilities, connecting various platforms for a comprehensive approach to traffic management.

II. RELATED WORKS

Intelligent Traffic Systems (ITS) leverage IoT applications to enhance traffic control. Studies explore innovative approaches such as the Green Wave System, triggering traffic signal changes based on emergency vehicle movements. However, challenges arise in adverse weather conditions affecting visual processing accuracy. RFID technology, employing tags without batteries, offers an alternative for emergency vehicle identification. Yet, issues persist in determining the necessity of signal changes when emergencies are not imminent.

Wireless Sensor Network (WSN) systems using magnetic sensors aim to improve vehicle detection accuracy. Proper vehicle detection is crucial for effective traffic management, considering lane occupancy, traffic flow, and speed. However, signal-to-noise ratio settings in WSN systems may impact detection reliability. Infrared and magnetic sensors combined enhance vehicle detection in intelligent transportation systems, but challenges persist in industrial zones.

Mobile-based traffic measurement devices present another alternative, monitoring and managing road traffic congestion. Lieskovsky and Badura propose an Intelligent Traffic System architecture with cameras at intersections, delivering real-time data through mobile Ad-hoc networks. Photoelectric sensors offer a method for controlling traffic signals, adjusting based on traffic flow and congestion.

III.  METHODOLOGY

The Intelligent Transportation Systems (ITS) discussed in this paper prioritize ordinary, stolen, and emergency vehicles with varying levels of importance. Short Message Services (SMS) dynamically update the database with vehicle priorities and categories. Stolen vehicle tracking involves GPS technology or RFID readers, overcoming challenges of weather conditions affecting reader signals.

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Conclusion

The paper explores various intelligent traffic management technologies, including smart phones, Green Wave Systems, RFID, and wireless connectivity with Big Data centers. Tables summarize applications, benefits, and drawbacks of each method. IoT technology enhances data collection precision, with mobile applications serving as a user interface for identifying and addressing traffic congestions. Intelligent Traffic Systems offer priority to emergency vehicles. Overall, the study presents a comprehensive overview of intelligent traffic management approaches, addressing challenges and proposing solutions for efficient and adaptive traffic control.

References

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Copyright

Copyright © 2023 Aakriti Sharma, Shivani Kumari, Ms. Neha Singh. 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.