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Enhancing Shared Mobility Through AI-Driven Bike Rental Platforms
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Abstract: The increasing demand for efficient urban mobility has led to the rise of digital platforms that provide affordable and flexible transportation options. Bike rental systems, in particular, offer a convenient solution for short- distance travel without the burden of ownership costs. RentBike is designed as a comprehensive web-based platform that connects riders, bike owners, and administrators within a unified system. The backend is developed using Flask to handle server-side operations efficiently, while MongoDB Atlas provides secure and scalable cloud-based data storage. The frontend is built using Tailwind CSS, ensuring a responsive and adaptive interface across different devices such as smartphones and desktops.
The platform integrates multiple functionalities including user authentication, bike search, booking management, payment processing, and review systems. Role-based access control ensures that users, vendors, and administrators have distinct permissions and capabilities within the system. Additionally, the platform incorporates a recommendation mechanism that analyzes user behavior and booking history to suggest suitable bikes. This personalized approach enhances user experience by presenting relevant options based on past interactions. The system operates seamlessly in the background, dynamically adapting to user roles and preferences without requiring explicit intervention.
From a technical perspective, the system is built using a modular architecture where different components interact through well-defined interfaces. Security is maintained using token-based authentication, while optimized database queries improve search performance and response time. The responsive design ensures accessibility across multiple devices, contributing to a smooth user experience. Performance testing indicates that the system can handle high loads efficiently while maintaining reliability and speed. Overall, RentBike demonstrates how modern web technologies can be leveraged to create scalable and intelligent mobility solutions, with potential future enhancements such as real-time tracking, dynamic pricing, and IoT integration.
Keywords: Flask Framework, JWT Authentication, Urban Mobility, RESTful APIs
The platform integrates multiple functionalities including user authentication, bike search, booking management, payment processing, and review systems. Role-based access control ensures that users, vendors, and administrators have distinct permissions and capabilities within the system. Additionally, the platform incorporates a recommendation mechanism that analyzes user behavior and booking history to suggest suitable bikes. This personalized approach enhances user experience by presenting relevant options based on past interactions. The system operates seamlessly in the background, dynamically adapting to user roles and preferences without requiring explicit intervention.
From a technical perspective, the system is built using a modular architecture where different components interact through well-defined interfaces. Security is maintained using token-based authentication, while optimized database queries improve search performance and response time. The responsive design ensures accessibility across multiple devices, contributing to a smooth user experience. Performance testing indicates that the system can handle high loads efficiently while maintaining reliability and speed. Overall, RentBike demonstrates how modern web technologies can be leveraged to create scalable and intelligent mobility solutions, with potential future enhancements such as real-time tracking, dynamic pricing, and IoT integration.
Keywords: Flask Framework, JWT Authentication, Urban Mobility, RESTful APIs
How to Cite:
[1] Prof. Diksha Bansod, Aaditi Katole, Janvi Aher, Sumit Ghoshal, Jay Ingole, βEnhancing Shared Mobility Through AI-Driven Bike Rental Platforms,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15498
