Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

Abstract

Technological developments create a lot of impacts in the tourism industry. Emerging big data technologies and programs generate opportunities to enhance the strategy and results for transport security. However, there is a difference between technological advances and their integration into the methods of tourism study. The rising popularity of Freycinet National Park led to a master plan that would not address cultural and environmental issues. This study addresses the gap by using a synthesized application (app) for demographic surveys and Global Navigation Satellite System (GNSS) technology to implement research processes. This article focuses on managing visitors within the famous Freycinet National Park. Extremely comprehensive structured data were analyzed in three phases, (1) identifying groups of visitors who are more likely to use the walking trails, (2) those who are more and less likely to visit during/peak crowding times, and (3) finally creating an integrated Spatio-temporal dependency model via a machine-based learning system for real-time activity. This research examines innovative techniques that can offer energy resources to managers and tourism agencies, especially in detecting, measuring, and potentially relieving crowding and over-tourism.