Name of the project
easyplanned
Introduced by
Pietro Visaggio
Description
The staff is made up of Visaggio Pietro (Destination Management), Mauro Fava (Front End and Back End developer), Marco Visaggio (Data Analyst), Michelangelo Goglia (Copywriter and Customer Care), Anna Monformoso (Marketing and Customer Care).
The solution is an application (ios and android) that allows the tourist to get a faster and easier travel planning process, with a level of optimal customization. For a better understanding of the solution, we have divided it into two phases: in the first phase, the algorithm suggests the attractions and/or destinations to the tourist through levels of importance expressed on four thematic areas: Art, Nature, Food and Crafts. Each proposed tourist attraction is associated with one of the three levels of importance, low, medium and high (varies al vary in the awards obtained, prestige, etc…).
According to the logic described above, with reference to “High interest” level art will therefore be proposed the most famous tourist attractions, with visit durations and high entrance costs, unlike “Low interest” level art which may be of little importance, with free admission can be visited in a short time.
This logic will allow us to suggest the right destinations to the tourist, because they are based on how much importance they have expressed to them, so the small destinations will be offered in equal measure to the large ones, thus limiting overtourism. In the second stage the algorithm will organize instantly the attractions chosen by the tourist for his next adventure. At every tourist attraction selected, the algorithm will try to match it with those already present in the calendar, taking into account the times of opening and closing, duration of visit, willingness to be booked, daily attendance levels, conditions weather, journey duration.
The aim is to save time on planning and optimize time at destination, avoiding unpleasant surprises such as closures unexpected events, sold out tickets and overcrowding. Especially on this last aspect, the algorithm will be able to suggest the right time to visit the structure by means of forecast data, avoiding overtourism. This methodology can be extended to group travel, because the algorithm uses the average arithmetic of the levels of importance expressed by single participant, is able to propose tourist attractions appropriate to the needs of the group and not the individual.
