This project was funded by the Research Center at Sri Sivasubramaniya Nadar College of Engineering (SSNCE), TN, India. It was carried out in collaboration with the Computer Science Department at SSNCE.
Please note that this work is part of an ongoing research project. Hence, some portions of the implementation code have not been made public through this repository. This will be pushed post project completion.
- IoT and ML to assuage the uncertainty associated with city bus commute.
- Allows commuters to track live running status and avail tentative schedule of buses.
- Live running status for buses in transit; Estimated schedule for planned future transits.
- IoT architecture with a central ML-driven web-backend.
If you find our work useful in your research, don't forget to cite us:
@article{desingu2023locatemybus,
author = {Karthik Desingu and Daniel Mark Isaac and Mirunalini P. and Bharathi B. and Cherry Mathew Philipose},
title = {LocateMyBus: IoT-Driven Smart Bus Transit},
journal = {Journal of Communications Software and Systems},
month = {4},
year = {2023},
volume = {19},
number = {2},
pages = {136--146},
doi = {10.24138/jcomss-2022-0143},
url = {https://doi.org/10.24138/jcomss-2022-0143}
}
Web-based user interface for live-tracking and tentative schedule display.
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Track live running status: Viewer interface to access the current running status of in-transit buses, accurate to the granularity of its last passed bus stop.
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Schedule prediction: Interface to display the estimated schedule of buses on a given future date, predicted by the proposed machine learning algorithm that incorporates dynamic day-specific factors with historical data.
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Search for buses between stops: Interface to inquire buses and schedules between two stops.
