# Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers +
## 4 Adaptive Cruise Tourist Itinerary Planning Problem  
The adaptive cruise tourist itinerary planning problem is similar to the more general tourist trip design problem (TTDP) [24]. This generic class of problems comprises a set of candidate points of interest (Pols) together with their associated attributes (e.g., type, location, timetable), travel time between Pols, user-dependent functions relative to Pols (e.g., satisfaction, expected duration), the trip time span, and the daily time limit. A daily schedule that meets the constraints imposed by POI properties and maximizes user satisfaction is expected to be proposed in a quality TTDP solution. The problem addressed in this paper is more challenging since the specific constraints to be considered for cruise passengers and city needs may change during the itinerary. An exemplification of the posed problem is illustrated in Figure 1. It shows a scenario in which the planned itinerary can not be fulfilled or is no longer deemed the optimal choice. This may happen for many unexpected reasons, such as a public transportation strike, a protest and/or unplanned building works preventing the transit in specific areas of the city, or overcrowding of a tourist attraction at a specific time. In this situation, a static planner is useless, and a human expert should re-plan a novel itinerary based on their experience, with no certainty that it will be the best.  
This paper proposes a DRL-based planner able to plan itineraries automatically through different points of interest that may dynamically change according to city conditions. The DRL agent can generalize the planning process to optimize and plan novel itineraries from any partial ones. Mainly, an itinerary is defined as a sequence of Pols under temporal and physical constraints, where _temporal_ means that the duration time of an itinerary must not exceed a fixed duration time depending on the cruise departure, and physical_ means that each PoI has a specified capacity and visiting time which must be taken into account at the time of choosing the PoIs.  
During the itinerary, changes in the city conditions may occur, affecting both the current receptivity of a PoI in correlation with its capacity and the time to travel from one PoI to another in relation to the city traffic conditions. This dynamic information on the city is assumed to be gathered through a sensor layer and notifications services (e.g., Google TrafficTM), which collect and process raw sensor data and signals from the environment.  
This paper refers to the city of Naples as a use-case scenario. The itineraries are built considering a set of Naples tourist attractions for fourteen points of interest. A touristic itinerary in Naples entails sequentially visiting various attractions, ultimately returning to the cruise. Each point of interest has a maximum number of people it can hold and a specific amount of time it takes to finish a visit. According to the problem, the greater the number of places tourists visit during a tour, the more enjoyable it is. However, the planner must consider overcrowding of attractions and ensure that the tourists have time to return to the port before departure.