Artificial Intelligence-Based Intelligent Navigation System for Alleviating Traffic Congestion: A Case Study in Batam City, Indonesia
Luki Hernando, Ririt Dwiputri Permatasari, Sri Dwi Ana Melia, M. Ansyar Bora, Alhamidi Alhamidi, Aulia Agung Dermawan · International Journal of Computational Methods and Experimental Measurements · 2025
Traffic congestion is a major issue faced by Batam, a city that continues to grow rapidly as an economic and logistics hub.This study adopts the Design Science Research Methodology (DSRM) to develop an intelligent navigation system based on artificial intelligence (AI) aimed at optimizing urban traffic management in Batam.The system integrates real-time traffic data, machine learning algorithms, and reinforcement learning to predict traffic flow and optimize route selection.Using the DSRM framework, the system was designed, implemented, and evaluated iteratively to ensure its effectiveness in addressing the city's unique traffic challenges.The results of the study indicate that the implementation of the AI-based navigation system successfully reduced the average travel time by 22.8%, distributed traffic loads more evenly, and improved travel efficiency.Furthermore, the system demonstrated a route prediction accuracy of 91.3%, higher than conventional GPS systems.Performance evaluation also showed high responsiveness, with an average latency of only 423 milliseconds.This study concludes that the AI-based navigation system, developed through the DSRM framework, can be an effective solution to address traffic congestion in rapidly developing cities like Batam and can be applied to other cities with similar characteristics.