A Graph-Based Smart Campus Navigation System Using A* Search and Multi-Criteria Route Evaluation

Abstract

Navigating large university campuses is challenging, especially for newcomers, due to complex multi-level indoor spaces connected by walkways. This study models a university campus as a weighted graph and employs an extended A* search algorithm to generate multiple candidate routes between locations. Unlike conventional approaches that compute a single optimal path, the proposed method returns several feasible routes. These routes are then evaluated using multi-criteria analysis considering distance, accessibility, and path simplicity, allowing users to select routes based on their preferences. A Pareto dominance-based filtering mechanism identifies nondominated paths, offering meaningful alternatives. The system follows a modular architecture comprising routing, spatial data processing, and visualization components. Experimental evaluation demonstrates that route computation occurs in microseconds, confirming real-time usability. The system reliably generates multiple valid paths while maintaining computational efficiency even as graph size increases. Results show that integrating multicriteria assessment into graph-based routing provides flexible, user-adaptive navigation without significant computational overhead.