Neural Network-Driven Tourism Experience Optimization and Dynamic Route Planning

Abstract

This study presents a dynamic route-planning framework implemented in Python based on an Elevated Beluga Whale Optimized Feed-Forward Backpropagation Neural Network (EBWO-FFBPNN). The dynamic route-planning framework optimizes the tourism experience by personalizing and dynamically re-adjusting route planning by utilizing a comprehensive data set. Data preprocessing involves normalization and data cleaning to ensure the quality of the input. Feature extraction is completed via Word2Vec for the semantic representation of the textual data, and topics are profiled using Latent Dirichlet Allocation (LDA) for travel and tourist interest. The core FFBPNN model seeks user behavior patterns and predicts route preference optimality. Real-time dynamic adjustment is achieved through EBWO-based dynamic optimization that adapts the route in real time according to the environmental context and user behavior. Experimental results demonstrate a precision of 0.98 and an F1 score of 0.96, indicating superior prediction accuracy, route efficiency, and personalization compared to conventional methods.