Parking Route Modeling Using the A* Algorithm for Density Reduction at the Faculty of Science and Technology, State Islamic University of North Sumatra

Abstract

The increasing number of vehicles on university campuses has led to significant congestion, particularly around parking areas. This study aims to design an intelligent parking route model using the Density-Aware A* algorithm to minimize vehicle congestion within the Faculty of Science and Technology (FST) at UIN North Sumatra. The proposed approach represents the internal campus network as a weighted graph, where each edge integrates both spatial distance and a density penalty that reflects the occupancy-to-capacity ratio of each parking area. The algorithm was implemented and simulated using Python and the NetworkX library within Google Colab. The results show that the system accurately identifies the optimal parking route based on vehicle type and real-time occupancy data. For motorcycles, the optimal path is A > B > F with a total cost of 23.06, while for cars, the most efficient path is A > B > H with a total cost of 18.21. The findings indicate that incorporating density-based cost adjustments effectively balances travel efficiency and vehicle distribution, contributing to overall congestion reduction in the FST–FKM corridor. Future research should focus on integrating live sensor data and adaptive feedback mechanisms to support large-scale deployment across diverse campus environments.