This technology is a method and server that generates travel plans—including travel time, estimated costs, and routes—by combining user-inputted departure/return times, main destinations, budgets, and transportation modes with big data-based supplementary information and real-time device location. It identifies and recommends common waypoints within search radii centered on the current location and the main destination, respectively.
Existing navigation services only provide the fastest or shortest routes between a starting point and a destination, forcing users to manually and complexly calculate their required budget and return times. This poses limitations for students with tight budgets or families with strict return time constraints, making it difficult for them to plan trips that include waypoints.
This technology identifies and displays overlapping waypoints from two search radii as a recommended group and recalculates the time, cost, and route when a user selects a waypoint. If a location is added during the trip, it determines the size of a third search radius based on the remaining time and budget, ensuring the plan stays within constraints even if the schedule changes. It can be applied to travel platforms, local tourism apps, and mobility service scheduling features to easily provide real-time, personalized itineraries that respect budget and return time constraints.
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