An AI-Powered Location-Based Recommendation System for Personalized Urban Mobility

Abstract

The increasing availability of heterogeneous spatial and contextual data sources presents both an opportunity and a challenge for location-based decision support systems. This paper presents an AI-powered recommendation system designed to facilitate personalized urban mobility while enhancing users' sense of place. The proposed system integrates multiple open data sources through a unified fusion pipeline that aligns various data to support location-based recommendations. Data sources include UK police crime statistics, Ticketmaster event listings, OpenStreetMap points of interest (POIs), online news articles, and routing data. The proposed system comprises four components: a dynamic scoring module for safety and popularity assessment, a conversational AI interface supporting natural language location queries, a personalized recommendation engine informed by user interaction behaviors, and a journey planner that provides users with ranked routes based on safety. The results confirm real-time responsiveness across all four components and contextually meaningful outputs across diverse query types. The key contributions of this work are: (i) a scalable, timely spatial data fusion pipeline for multi-source urban data; and (ii) an AI-augmented decision support framework that promotes more informed and safer urban mobility.