Personalized Path Planning Estimation of Georeferenced Urban Scene Considering Criminality Map: The Case of Attica

This paper introduces an advanced georeferenced path planning methodology that enhances urban navigation by incorporating crime statistics to ensure safety and adherence to user preferences. Leveraging a combination of Geographic Information Systems (GIS) and 3D urban modeling, our approach proposes a unique platform that personalizes route planning within the urban landscape of Attica, Greece. Our approach is taking into consideration user’s preferences considering qualitative and quantitative features and a Criminality Map. Initially the user profile is estimated based on a pairwise comparison approach of inductive-based learning methodology using Analytical Hierarchy Process (AHP). The proposed platform provides a personalized path planning estimation for urban areas by aligning geographic data with both user-specific preferences and safety considerations. The efficacy of our approach is demonstrated through a detailed case study of Attica (Greece), where over 700 georeferenced models are analyzed for path planning alongside with criminal reports of Police for the year 2022. This research highlights the potential of combining safety metrics with personalization in path planning, offering significant implications for the development of smart navigation systems in urban environments.