Vol. 2 No. 07 (2025): Real-Time Bus Arrival Prediction Using Machine Learning and GPS

Abstract

The proposed project aims to develop a robust Bus Arrival Time Prediction and Tracking system utilizing machine learning techniques. The primary objective is to enhance the efficiency and reliability of public transportation by accurately predicting bus arrival times based on historical data and real-time information. The project involves the creation of a machine learning model trained on a comprehensive dataset that includes factors such as traffic conditions, weather, and past bus performance.

The system integrates GPS tracking technology and other relevant sensors to continuously update the model in real-time, ensuring precise predictions that adapt to dynamic conditions. The project aims to improve the overall commuter experience by minimizing waiting times and providing passengers with timely and reliable information.

Key components of the project include data collection and preprocessing, machine learning model development and training, integration with real-time tracking technologies, and the implementation of a user-friendly interface for passengers to access predicted arrival times. The successful completion of this project will contribute to the advancement of smart transportation systems, fostering efficiency and user satisfaction in public bus services.

Index terms

Bus Arrival Time Prediction, Tracking System, Machine Learning Techniques, Public Transportation, Efficiency, Reliability, Historical Data, Real-time Information, GPS Tracking Technology, Sensors, Dynamic Conditions, Commuter Experience, Waiting Times, Timely Information, Data Collection, Preprocessing, Model Development, Model Training, Real-time Tracking, User-friendly Interface, Smart Transportation Systems, User Satisfaction, Public Bus Services.