PREFERENCE-BASED VEHICLE SELECTION INTERFACE IN ONLINE CAB BOOKING

Abstract: The evolution of online cab booking services has significantly transformed urban transportation, yet many existing platforms lack the ability to provide personalized vehicle selection based on user preferences. This study introduces an advanced preference-based vehicle selection interface that enhances the online cab booking experience by allowing users to filter vehicles based on factors such as comfort, cost, eco-friendliness, seating capacity, and additional features like Wi-Fi and air conditioning. The proposed system integrates a dynamic filtering mechanism that enables users to make informed decisions by offering real-time vehicle availability, detailed specifications, and an option to select a preferred driver based on gender. The interface is designed to provide a seamless, secure, and efficient booking experience while prioritizing accessibility and sustainability. By leveraging technologies such as HTML5, CSS3, JavaScript for the front end, Node.js for the back end, and MySQL/MongoDB for database management, the platform ensures a robust and scalable system. This research highlights the necessity of customization in online cab booking to improve user satisfaction and address the limitations of existing platforms. Future enhancements may include artificial intelligence-driven recommendations, integration with real-time traffic data for better ride estimations, and further expansion of accessibility features to cater to a broader range of user needs. The preference-based selection model presented in this study has the potential to revolutionize online cab booking services by bridging the gap between user expectations and current industry standards.

Keywords: Online Cab Booking, Vehicle Selection, User Preferences, Personalized Filters, Ride Customization, Real- time Availability, Transportation Efficiency, Eco-friendly Vehicles, Accessibility, AI-driven Recommendations