Inclusive Mobility for Normal and Blind Users- Travid

Public mobility applications in modern cities remain largely inaccessible to users with visual impairments because most are heavily dependent on visual navigation and manual typing. To address this gap, the proposed work introduces Travid, a voice-enabled travel assistant designed to provide inclusive navigation support for both sighted and visually impaired users. Developed using the Flutter framework, the system integrates Speech-to-Text (STT) and Text-to-Speech (TTS) technologies to create a natural conversational interface. The application accepts spoken commands to search bus routes, navigate maps, and manage user profiles through voice-based interaction. The incorporation of fuzzy string matching ensures tolerance to speech recognition inaccuracies and mispronunciations. By combining offline data handling with accessible design, Travid achieves low-latency operation, data privacy, and an improved user experience. The project demonstrates that an inclusive, hybrid mobile system can effectively combine voice input, map visualization, and local data storage to support independent travel.