Intelligent Planning of Onshore Touristic Itineraries for Cruise Passengers in a Smart City

Typical cruise-line itineraries are planned more than a year in advance, so it is possible to know how many passengers arrive in advance months ahead, as well as when they leave and for how long they stay. Nevertheless, cruise passengers are often responsible of what is called “tourism crowding” that has a beneficial economic outcome for a destination, but often at the cost of increment of city traffic, overloading of the public transportation means and discomfort generated for residents. So, cruise tourism requires planning and management that takes into account also port city destinations factors. In this paper, we present a Deep Reinforcement Learning based planner for the onshore touristic itineraries and the intelligent distribution of cruise passengers in a city. The aim is to maximise the number of touristic attraction locations visited during a tour by avoiding the overcrowding of the touristic attraction locations. The planner is able to compose onshore touristic itineraries meeting the constraint given within the time window available for the cruise passengers, by taking into account the number of attraction locations available in the city together with dynamic parameters as their reception capacity and the time needed to go from one attraction to another.