A Machine Learning Approach to Parking Systems with Bidirectional Search and A* Algorithm for Optimized Pathfinding

Description

This study addresses the problem of inefficient parking search and route navigation in urban environments with limited real-time parking data. Titled “A Machine Learning Approach to Parking Systems with Bidirectional Search and A Algorithms for Optimized Pathfinding,”* it aims to recommend suitable parking areas and generate efficient routes using machine learning–based suitability ranking and graph-based pathfinding algorithms. Designed for urban drivers and parking system administrators, the system evaluates parking options based on distance, cost, safety, accessibility, and operating hours, while computing optimal routes using Bidirectional A*. It is expected to reduce parking search time, traffic congestion, and user decision effort, contributing to smarter, more sustainable urban mobility solutions.