Open Access

ARTICLE

Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

L. Maria Michael Visuwasam^1,*, D. Paul Raj²

1 Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, 600104, India
2 Department of Computer Science and Engineering, R. M. D. Engineering College, Chennai, Tamil Nadu, 601204, India

* Corresponding Author: L. Maria Michael Visuwasam. Email:

Computer Systems Science and Engineering 2023, 45(3), 2435-2446. https://doi.org/10.32604/csse.2023.024742

Received 29 October 2021; Accepted 15 February 2022; Issue published 21 December 2022

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.

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