Cutting-Edge Travel Planner: Intelligent Route Recommendation System using Enhanced Learning Scheme with AI Principles

A route planner is crucial as it optimizes travel efficiency, minimizes time and fuel consumption, and enhances overall navigation convenience and safety. This paper presents the design and implementation of CollabRouteNet, an intelligent route recommendation system that leverages collaborative filtering and reinforcement learning principles. The proposed model aims to provide personalized and contextually relevant route recommendations tailored to individual user preferences and dynamic environmental conditions. Collaborative filtering techniques are employed to analyze user interactions and discern patterns within historical route data. This involves constructing a user-item matrix and applying matrix factorization to learn latent representations for users and routes. In parallel, reinforcement learning is utilized to optimize route recommendations in real-time by defining the problem as a Markov Decision Process (MDP) and training an agent to learn an optimal route selection policy. The model balances exploration and exploitation to adaptively recommend routes that optimize user satisfaction and navigation efficiency. Implemented in PyCharm, the CollabRouteNet model demonstrates promising results in providing accurate and responsive route recommendations. Through its integrated approach, CollabRouteNet offers a promising solution for enhancing navigation experiences in diverse urban and rural settings.