Customized Tourist Navigation in Urban Contexts Following General Trends and Individual Preferences

Abstract

Navigating an unfamiliar city poses significant challenges, and tourists are among the groups more likely to experience them, particularly when attempting to locate a point of interest (POI). Various factors, such as language barriers or a lack of precise information, further complicate this issue by making it difficult for visitors to explore efficiently. To address these issues, we introduce the tool "Personalized Assistant for Tourist Hints" (PATH), which is based on a methodology for estimating personalized tourist routes. PATH computes optimal paths to specified locations guided by two tailored heuristics: tourist frequency and POI preference. This integration enables efficient and personalized navigation, guiding users toward attractions that best match their preferences. We evaluated our methodology through a case study in Viterbo, Italy, using a dataset comprising over 1000 routes from 265 tourists who visited the city during different periods. To validate our approach, we tested it with 238 new tourists and collected expert assessments from 86 local specialists. User satisfaction was measured through a structured questionnaire, which revealed highly positive feedback regarding the relevance and quality of the recommended routes, also when compared with existing routing applications and approaches proposed in the literature. These results demonstrate PATH's potential to enhance urban exploration by providing an adaptable and user-centric solution for tourist navigation.