Enhancing Pathfinding Optimization for Mode of Transportation with in Intramuros for Improved Efficiency and Navigation Using the A* Algorithm

The study aims to develop an advanced AI navigation app designed to assist drivers in finding efficient routes within Intramuros. The study focuses on developing a user-friendly mobile application that offers transparent fare calculations using Linear Regression, allowing passengers and drivers to track and understand fare breakdowns. Additionally, the study incorporates a system that presents multiple route options to various historical sites within Intramuros, providing accurate predictions for travel time and distance using the Random Forest Regressor. Finally, the implementation of an AI-driven navigation app utilizing the A* algorithm aims to optimize navigation through elevated routes within Intramuros, offering precise and dynamic directions that adapt to changing road conditions, ensuring smoother and faster journeys. This study aimed to survey drivers in Intramuros and IT professionals to evaluate the effectiveness of the developed mobile application. The researchers used interview questions and rating sheet surveys, which were conducted as part of the research. Statistical tools, such as the four-point Likert scale, were employed to assess the functional suitability, reliability, usability, and performance efficiency of the system. The results for functional suitability are 3.08, reliability is 3.04, useability is 3.18, and performance efficiency is 3.09. The overall result is 3.1, which signifies that the app has demonstrated good performance across all evaluated criteria