# Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers +
## 1 Introduction  
Over the course of several decades, numerous port cities throughout Europe have made significant investments in the renovation of their waterfront areas and the revitalization of their historic centers. As a consequence of these efforts, these cities have emerged as highly desirable tourist destinations and have established themselves as prominent players within the global tourist industry rankings [1]. For instance, several cities, such as Venice and Barcelona, serve as home ports for a plethora of shipping enterprises. Several other cities, including Lisbon, Genoa, and Naples, have directed their attention towards enhancing cultural offerings to establish themselves as prominent ports along key cruise itineraries [2]. Cities are progressively implementing marketing strategies aimed at attracting a greater volume of cruise passengers. As a consequence, in recent years, European port cities have experienced an influx of mass tourism driven by the emergence of increasingly larger cruise ships and low-cost tourism. This phenomenon, commonly known as touristification, severely threatens these destinations' sustainability [3, 4]. The World Tourism Organization UNWTO [5] defines this tourist overcrowding or overtourism as \"_the impact of tourism on a destination, or parts thereof, that excessively influences the perceived quality of life of citizens and/or quality of visitor experiences in a negative way_\". The phenomenon of cruise tourism has been identified as a noteworthy source of concern in urban centers such as Amsterdam, Barcelona, Naples, and Venice. The significant number of visitors that converge on the cities within prescribed schedules leads tocongestion in transportation infrastructure and the preeminent attraction sites. Consequently, the impact of cruise tourism on urban spaces and their associated facilities has emerged as a critical issue [2]. Among several strategies, intelligent and adaptive planning of touristic itineraries, which propose alternative itineraries considering the city's dynamic situation and unexpected events such as a public transportation strike, a protest, traffic, or points-of-interest congestion, may help mitigate tourist overcrowding. In this direction, this paper proposes an autonomous dynamic planner for touristic itineraries in the smart city context designed to enhance the quality of the tourists' experience following the prevailing conditions of the urban environment. We propose a new autonomous dynamic planner that seeks to find the best route for a one-day cruise traveler. From a tourist's perspective, the objective is to maximize the number of points of interest (PoIs) to be visited during both the outbound and return journey with the view. The proposed autonomous dynamic planner aims to define the optimal path of a one-day cruise tourist, maximizing as many points of interest as possible in the outer and back routes. At the same time, the proposed planner considers the hard time constraints of the cruise and the dynamic city conditions affecting the travel time from one PoI to another and their current receptivity. In so doing, real-time information from the city, such as traffic information, availability of public resources, and reception capacity of tourist attraction sites, are included in the planner. Moreover, the planner is designed to adapt the city itinerary to several unexpected events to improve the tourist-positive experience of the city and mitigate the contemporary tourist overcrowding of congested city areas and points of interest. The proposed dynamic planner is mainly based on a deep reinforcement learning (DRL) algorithm that employs run-time information during the learning process. An experimental evaluation was carried out to assess the proposed approach's performance in providing alternative itineraries under hard time constraints. A deeper analysis and an evaluation of itinerary planning performance are also addressed, outlining new experiments and more detailed results than the pilot experiment reported in [6].