Authors

Thammineni Medara Veeresh Babu

UG Student, Department of Electronics and Communication Engineering, Gates Institute of Technology, Gooty, Anantapur, Andhra Pradesh, India

Chikkudu Blessy

Assistant Professor, Department of Electronics and Communication Engineering, Gates Institute of Technology, Gooty, Anantapur, Andhra Pradesh, India

Medara Sharvani

UG Student, Department of Electronics and Communication Engineering, Gates Institute of Technology, Gooty, Anantapur, Andhra Pradesh, India

Venkateshwara Surendra

UG Student, Department of Electronics and Communication Engineering, Gates Institute of Technology, Gooty, Anantapur, Andhra Pradesh, India

Myla Swapna

UG Student, Department of Electronics and Communication Engineering, Gates Institute of Technology, Gooty, Anantapur, Andhra Pradesh, India

Golla Sunil Kumar

UG Student, Department of Electronics and Communication Engineering, Gates Institute of Technology, Gooty, Anantapur, Andhra Pradesh, India

Abstract

Efficient public transport tracking and delivery management are crucial for reducing delays, improving passenger experience, and optimizing logistics operations. This project proposes a Raspberry Pi-based real-time public transport tracker and delivery system that integrates sensor technology and AI-driven analytics for enhanced monitoring and security.

The system utilizes a camera for real-time visual monitoring, while the MQ135 and MQ7 gas sensors detect air quality and harmful gases within the transport environment. An IR sensor assists in tracking vehicle movement and passenger entry/exit, ensuring real-time occupancy monitoring. The DHT11 sensor measures temperature and humidity, providing environmental insights for passenger comfort and package safety.

A buzzer serves as an alert mechanism for emergency situations, such as unauthorized access or hazardous gas detection. The ADC modem converts sensor data for seamless processing by Raspberry Pi 5, which acts as the central control unit. Data is stored on an SD card for analysis and record-keeping, enabling improved route optimization and predictive maintenance.

This system provides real-time tracking, security enhancements, and environmental monitoring for public transport and delivery services. By integrating IoT and AI technologies, it ensures efficient fleet management, enhanced passenger safety, and improved delivery operations, making it a scalable and cost-effective solution for smart transportation systems.

Keywords

Camera MQ 135 IR Sensor DTH 11 MQ7 Buzzer ADC modem SD card Raspberry-pi 5

Citation of this Article

Thammineni Medara Veeresh Babu, Chikkudu Blessy, Medara Sharvani, Venkateshwara Surendra, Myla Swapna, & Golla Sunil Kumar. (2025). Raspberry-Pi Based Real-Time Public Transport Tracker and Delivery System. International Current Journal of Engineering and Science - ICJES, 4(4), 18-23. Article DOI: https://doi.org/10.47001/ICJES/2025.404003

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. J. Zawieska and J. Pieriegud, "Smart city as a tool for sustainable mobility and transport decarbonisation", Transp. Policy, vol. 63, pp. 39-50, Apr. 2018.
  2. H. Abigail, "Americans spend over 15% of their budgets on transportation costs-these US cities are trying to make it free", Mar. 2020, [online] Available: http://www.wearetdm.com.
  3. Tembe, F. Nakamura, S. Tanaka, R. Ariyoshi and S. Miura, "The demand for public buses in sub-Saharan African cities: Case studies from Maputo and Nairobi", IATSS Res., vol. 43, no. 2, pp. 122-130, Jul. 2019.
  4. R. Basu, A. Araldo, A. P. Akkinepally, B. H. N. Biran, K. Basak, R. Seshadri, et al., "Automated mobility-on-demand vs. mass transit: A multi-modal activity-driven agent-based simulation approach", Transp. Res. Rec. J. Transp. Res. Board, vol. 2672, no. 8, pp. 608-618, Dec. 2018.
  5. Tram—Definition-The Free Dictionary, Princeton, NJ, USA, Feb. 2018.
  6. China’s Metro Boom Continues to Drive Rapid Transit Growth, New York, NY, USA, Jul. 2018.
  7. Y.-S. Hwang, J.-W. An and J.-M. Lee, "The standard for the selection of the appropriate GPS in the outdoor environment the analysis of the performance for the improvement of reception", Proc. IEEE Int. Conf. Adv. Intell. Mechatronics (AIM), pp. 852-857, Jul. 2016.
  8. J.-W. An and J.-M. Lee, "Improvement of GPS position estimation using SNR and Doppler", Proc. IEEE Int. Conf. Adv. Intell. Mechatronics (AIM), pp. 1645-1650, Jul. 2017.
  9. D. P. McArthur and J. Hong, "Visualising where commuting cyclists travel using crowdsourced data", J. Transp. Geography, vol. 74, pp. 233-241, Jan. 2019.
  10. L. Xu, L. Wang, Y. Zhang and S. Cheng, "Visual tracking based on siamese network of fused score map", IEEE Access, vol. 7, pp. 151389-151398, 2019.