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Smart Bus: Crowd-Prediction System
Subject area: Science,Engineering and Technology · Area of research: IoT
DOI: 10.64388/IREV9I12-1718715
Abstract
The Smart Bus Crowd Prediction System is an intelligent transportation solution designed to improve passenger convenience and public bus management using Machine Learning (ML), Internet of Things (IoT), Web Technologies, and R Programming. The system collects real-time data from sensors, GPS modules, and passenger inputs to monitor bus occupancy, location, and travel patterns. Machine learning algorithms are used to analyze historical and live data to predict crowd density and bus arrival timings with improved accuracy.A web-based platform is developed to display live bus tracking, predicted crowd levels, and estimated arrival times to passengers and transport authorities. R programming is utilized for data analysis, visualization, and statistical prediction of passenger flow trends. IoT devices enable continuous communication between buses and the central server, ensuring real-time updates and efficient data handling. The proposed system helps passengers avoid overcrowded buses, reduces waiting time, and improves travel planning. It also assists transport authorities in optimizing bus scheduling and resource allocation. By integrating intelligent prediction models with modern web and IoT technologies, the project aims to create a smarter, more efficient, and user-friendly public transportation system.
Keywords
Smart Bus System, Crowd Prediction, Machine Learning, Internet of Things (IoT), Web Development, R Programming, Real-time Data Analytics, Public Transportation, Bus Tracking System, Data Visualization, Predictive Modeling, Smart Mobility
References
[1] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. https://www.springer.com/gp/book/9780387310732
[2] P. Raj and A. Raman, The Internet of Things: Enabling Technologies, Platforms, and Use Cases, CRC Press, 2017. https://www.routledge.com/The-Internet-of-Things-Enabling-Technologies-Platforms-and-Use-Cases/Raj-Raman/p/book/9781498778512
[3] Zanella, A. et al., “Internet of Things for Smart Cities,” IEEE Internet of Things Journal. https://ieeexplore.ieee.org/document/6740844
[4] “Bus Arrival Time Prediction Using Machine Learning Techniques,” IEEE Access. https://ieeexplore.ieee.org/document/9388335
[5] MDPI Smart Cities Journal–Real-TimePassenger Information Systems https://www.mdpi.com/2624-6511/6/2/35
[6] Google Scholar – Smart Bus Crowd Prediction Research Papers https://scholar.google.com/scholar?q=smart+bus+crowd+prediction+IoT+machine+learning
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[9] Dr.J.Narendra Babu, et.al, Journal of Internet Services and information security, AI-Enabled Forecasting and Isolation Forest-Based Detection of CBF Flow Anomalies in Secure Internet Architectures, Year 2025, Volume: 15, number: 3 (August).Q2 Scopus Journal
[10] J. Narendra Babu, et.al– Indian License Plate Recognition System Based on Fuzzy Theory and BP Neural Network
How to cite this paper
@article{1718715,
author = {Dr. J. Narendra Babu, Dr. Deepak S Sakkari, Swati Jha, Varsha V, Srishti Singh; Yashaswini V},
title = {Smart Bus: Crowd-Prediction System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {1261-1265},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1718715.pdf},
abstract = {The Smart Bus Crowd Prediction System is an intelligent transportation solution designed to improve passenger convenience and public bus management using Machine Learning (ML), Internet of Things (IoT), Web Technologies, and R Programming. The system collects real-time data from sensors, GPS modules, and passenger inputs to monitor bus occupancy, location, and travel patterns. Machine learning algorithms are used to analyze historical and live data to predict crowd density and bus arrival timings with improved accuracy.A web-based platform is developed to display live bus tracking, predicted crowd levels, and estimated arrival times to passengers and transport authorities. R programming is utilized for data analysis, visualization, and statistical prediction of passenger flow trends. IoT devices enable continuous communication between buses and the central server, ensuring real-time updates and efficient data handling. The proposed system helps passengers avoid overcrowded buses, reduces waiting time, and improves travel planning. It also assists transport authorities in optimizing bus scheduling and resource allocation. By integrating intelligent prediction models with modern web and IoT technologies, the project aims to create a smarter, more efficient, and user-friendly public transportation system.},
keywords = {Smart Bus System, Crowd Prediction, Machine Learning, Internet of Things (IoT), Web Development, R Programming, Real-time Data Analytics, Public Transportation, Bus Tracking System, Data Visualization, Predictive Modeling, Smart Mobility},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1718715}
}