Smart Shuttle System: An IoT and Machine Learning Based Approach

Year : 2023 | Volume : 01 | Issue : 01 | Page : 9-22
By

    M. Divya Teja

  1. Nuthalapati Ganesh

  2. Keerthi Uppalapati

  1. Student, Department of Electronics and Communication Engineering, Vellore Institute of Technology, Katpadi, Vellore, Tamil Nadu, India, Tamilnadu, India
  2. Student, Department of Electronics and Communication Engineering, Vellore Institute of Technology, Katpadi, Vellore, Tamil Nadu, India, Tamilnadu, India
  3. Student, Department of Electronics and Communication Engineering, Vellore Institute of Technology, Katpadi, Vellore, Tamil Nadu, India, Tamilnadu, India

Abstract

The Smart Shuttle System is an IoT-based system designed to provide a hassle-free, cashless payment experience for students using the shuttle service. The system utilizes RFID (Radio Frequency Identification) technology to identify students and deduct the fare amount from their account. Radio Frequency Identification (RFID) technology is being considered as an alternative to traditional money and Unified Payments Interface (UPI) for making payments due to several advantages it provides over traditional methods. RFID tags provide a more convenient and efficient way of making payments compared to traditional methods like cash or UPI, which require users to have access to a mobile device or a bank account. RFID tags can be easily carried in a wallet or purse and can be used to make payments with just a tap. The system also incorporates machine learning algorithms to predict income for a particular date. This paper presents the design and implementation of the Smart Shuttle System, including hardware setup, software installation, RFID tag registration, Wi-Fi communication, balance verification, data logging, and web interface. The results of the system performance show that it can handle up to 23 transactions in 90 seconds, providing a fast and efficient payment method for students.

Keywords: RFID (Radio Frequency Identification), GPS(Global Positioning System), Wi-Fi module, cost-efficient, time efficient, ANN (Artificial Neural Network), UPI (Unified Payment Interface), BRT (Bias Resistor built in transistor).

[This article belongs to International Journal of Satellite Remote Sensing(ijsrs)]

How to cite this article: M. Divya Teja, Nuthalapati Ganesh, Keerthi Uppalapati Smart Shuttle System: An IoT and Machine Learning Based Approach ijsrs 2023; 01:9-22
How to cite this URL: M. Divya Teja, Nuthalapati Ganesh, Keerthi Uppalapati Smart Shuttle System: An IoT and Machine Learning Based Approach ijsrs 2023 {cited 2023 Oct 26};01:9-22. Available from: https://journals.stmjournals.com/ijsrs/article=2023/view=129652

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Regular Issue Subscription Original Research
Volume 01
Issue 01
Received October 7, 2023
Accepted October 14, 2023
Published October 26, 2023