KSRTC  LIVE  BUS -  SMART  TRANSIT  MONITORING  SYSTEM

Divya  S Sanningannanavar; Swathi D Mahindrakar

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Authors

Divya S Sanningannanavar Student, Master of Computer Applications, School of Computer Applications, GM University, Davanagere, Karnataka, India
Author
Swathi D Mahindrakar Assistant Professor & PG Coordinator, Department of MCA, GM University, Davanagere, Karnataka, India
Author

DOI:

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Keywords:

Real-Time Bus Tracking, Estimated Time of Arrival (ETA), GPS, AI-based Predictive Model, Intelligent Transportation Systems (ITS), Public Transportation.

Abstract

Public transportation passengers often face uncer- tainty regarding bus arrival times, as traditional static schedules fail to account for real-world issues such as traffic congestion, ve- hicle breakdowns, and route diversions. To address this challenge, this paper proposes a Real-Time Bus Tracking and Estimated Time of Arrival (ETA) Prediction System for the Karnataka State Road Transport Corporation (KSRTC). The system inte- grates IoT-enabled GPS devices to continuously capture the live location of buses, while an AI-based predictive model calculates accurate ETA by analyzing historical traffic data and current conditions. A user-friendly web and mobile application allows passengers to track buses in real time, view ETA updates, and plan their journeys more efficiently. In addition, the system provides administrators with fleet monitoring tools to improve operational efficiency, ensure service reliability, and enhance passenger satisfaction. This research contributes to Intelligent Transportation Systems (ITS) by presenting a data-driven public transportation solution tailored for Indian transit networks.