AI-Based Intelligent Ride Sharing Platform

Abstract

Urban transportation systems are increasingly adopting ride-sharing solutions to reduce traffic congestion and improve mobility. However, traditional ride-sharing platforms face issues such as inefficient driver allocation, surge pricing imbalance, safety concerns, and lack of predictive analytics. This research proposes an AI-based intelligent ride-sharing platform developed as a mobile application using Java for Android, Swift for iOS, and Firebase as the real-time cloud database. The system integrates machine learning algorithms for smart ride matching, dynamic pricing, demand forecasting, and safety monitoring. The proposed solution enhances operational efficiency, reduces waiting time, and improves user satisfaction through intelligent automation and real-time analytics.

Introduction

Rapid urbanization and population growth have increased transportation demand in cities, leading to problems such as traffic congestion, pollution, fuel wastage, and longer travel times. Ride-sharing platforms offer a technology-based solution by connecting passengers and drivers through mobile applications using GPS, cloud computing, and data analytics. The integration of Artificial Intelligence (AI) further improves efficiency by reducing waiting time, optimizing routes, and balancing driver availability.

However, current ride-sharing systems still face issues like long passenger waiting times, driver-passenger mismatches, surge pricing imbalance, ride cancellations, safety concerns, and poor demand prediction. To address these problems, the proposed system develops an AI-integrated ride-sharing platform with cross-platform mobile applications for Android (Java) and iOS (Swift), supported by a cloud backend using Firebase.

The system architecture includes a mobile application layer for riders and drivers, a backend database for real-time data synchronization, and an AI module for smart ride matching, dynamic pricing, demand forecasting, and fraud detection. The development process involves requirement analysis, system design, application development, AI model integration, testing, and deployment on mobile app stores.

The system offers several advantages such as reduced passenger waiting time, optimized driver allocation, improved safety, real-time data synchronization, and scalable cloud architecture. Testing results show successful implementation of features like user authentication, ride booking, live tracking, driver allocation, notifications, chatbot support, and earnings dashboard. Future improvements may include integration with autonomous vehicles, smart traffic systems, electric vehicle optimization, blockchain payments, and multi-modal transportation systems.

Conclusion

The AI-Based Intelligent Ride Sharing Platform developed using Java, Swift, and Firebase provides a scalable and efficient transportation solution. By integrating machine learning algorithms and real-time cloud database services, the system enhances ride allocation, pricing strategies, and safety monitoring. The proposed platform improves user satisfaction and contributes to smarter urban mobility systems.

References

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Copyright

Copyright © 2026 Gayatri Kailas Gadekar, Purva Nilesh Dumbre, Vedika Rahul Karanjkhele, Mayuri Jayram Chavare, Prof. Vishaka Hande. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.