Authors: Harsh Manvani, Niraj Shrimali, Sahil Ahir, Ved Patel
Abstract: The tourism sector is changing quickly as digital tools be- come more common, but many travelers still struggle to plan trips that are both convenient and affordable. A major problem is the absence of a single platform that can offer clear, reliable, and budget-focused itineraries. Although there is plenty of information available on travel websites and search engines, it is scattered and often difficult to com- pare, which makes the planning process tiring and confusing.Another issue appears once tourists reach their destination. Local transport frequently becomes a challenge, especially for first-time visitors who may end up paying high prices or relying only on taxis and ride-hailing services. This also reduces their chances of exploring places more freely and experiencing the local culture. In many tourist locations, two-wheeler rentals are actually a more convenient and enjoyable way to move around, yet they are not well integrated into existing travel systems.To overcome these problems, this study proposes an AI-based platform that combines personalized trip planning with a peer-to-peer bike rental service. The system uses machine learning to create tailored itineraries by collecting and organizing travel data from multiple sources. At the same time, it offers a safe and easy-to-use marketplace where travelers can rent two-wheelers from local owners. By bringing together artificial intelligence and community-driven mobility options, the platform aims to make travel more accessible, reduce instances of overcharging, and improve the overall experience for tourists.
DOI: http://doi.org/10.5281/zenodo.17746212
