Mobile GIS for Smart Urban Tourism: Multi-Stop Route Optimization in Abu Dhabi

Abstract. This study introduces a Mobile GIS framework designed to enhance urban tourism experiences in Abu Dhabi by integrating open-source spatial data with an innovative multi-stop route optimization algorithm. The system enables tourists to efficiently navigate and explore points of interest while dynamically generating optimized itineraries based on user preferences, temporal constraints, and spatial context. Leveraging Python geospatial libraries including GeoPandas, OSMnx, NetworkX, and Folium, the platform processes complex urban networks—demonstrated through 6,635 street segments spanning 416.1 km within a 16.1 km² study area— to produce efficient tourist routes. Quantitative results show the algorithm achieves a routing efficiency of 9.5 POIs per kilometer within a 4.44 km optimized path, significantly outperforming conventional navigation platforms in attraction coverage. The framework incorporates interactive visualization, offline accessibility, and standardized GIS exports (GeoPackage) for broader compatibility. Comparative analysis with existing solutions highlights superior multi-stop optimization capabilities and seamless integration of diverse datasets. This approach demonstrates scalability, computational efficiency, and adaptability for future smart tourism applications, including augmented reality features and adaptive recommendation systems. By bridging technical innovation with practical tourism needs, the framework contributes to sustainable, data-driven urban tourism development while offering extensible architecture for urban planning, crisis management, and service optimization applications.