Real-Time Bus Tracking and Management System with IOT, Chatbot Integration and Accident Zone Prediction

Abstract

In local areas, bus management is mostly manual, with different individuals responsible for tracking, monitoring, and reporting. The lack of standardized operational procedures leads to inefficiencies and inconsistent service delivery.  To address these challenges, a Real-Time Bus Tracking and Management System has been developed, incorporating modern technologies such as the Internet of Things (IoT), Global Positioning System (GPS), and Global System for Mobile Communications (GSM), along with a Machine Learning-based Chatbot and Accident Zone Prediction mechanism. A GPS tracker embedded in each bus provides real-time location updates, while GSM modules ensure data transmission even in areas with poor internet connectivity Passengers can use their mobile app to view bus schedules, monitor live bus locations, and receive notifications regarding estimated arrival times and route deviations, improving their overall commuting experience. Additionally, the system incorporates a Machine Learning model to enhance commuter safety. By training a Support Vector Machine (SVM) on historical accident data, including area names, GPS coordinates, and incident frequency, the system identifies and predicts accident-prone zones. This allows for proactive route planning and risk mitigation by transport authorities. The integration of IoT, GPS, GSM, and AI components ensures a ccomprehensive solution for efficient, safe, and intelligent bus management in real-time.