COVID-19 has greatly affected the tourist industry and ways of travel. According to the UNTWO predictions, the number of international tourist arrivals will be slowly growing by the end of 2021. One of the ways to keep tourists safe during travel is to use a personal car or car-sharing service. The sensor-based information collected from the tourist’s smartphone during the trip allows his/her behaviour analysis. For this purpose, we propose to use the Internet of Things with ambient intelligence technologies, which allows information processing using the surrounding devices. The paper describes a solution to the car tourist trajectory prediction, which has been the demanding subject of different research studies in recent years. We present an approach based on the usage of the bidirectional LSTM neural network model. We show the reference model of the tourist support system for car-based attraction-visiting trips. The sensor data acquisition process and the bidirectional LSTM model construction, training and evaluation are demonstrated. We propose a system architecture that uses the tourist’s smartphone for data acquisition as well as more powerful surrounding devices for information processing. The obtained results can be used for tourist trip behaviour analysis.

Car tourist trajectory prediction based on bidirectional lstm neural network

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

Tourism industry has been actively developing during recent years. Tourists actively produce user-generated content such as photos and videos, use various mobile devices to support, and share their attractions review trips in social networks. This content is a basis for tourist behaviour models construction which allow to predict future desires and intentions. This work presents a tourist behaviour analysis system based on digital pattern of life concept. This concept represents tourist in IT environment and connects them with behaviour analysis instruments. The solution is based on ontological approach, which allows to use context information for the analysis and prediction. The usage of digital pattern of life allows to extract tourists behaviour components in a convenient form for analysis. The paper introduces three behaviour analysis use cases, which can be implemented by using classification, clustering, and time-series prediction machine learning techniques.

Tourist Behaviour Analysis Based on Digital Pattern of Life

The tourism industry has been rapidly growing over the last years and IT technologies have had a great affect on tourists as well. Tourist behaviour analysis has been the subject of different research studies in recent years. This paper presents the digital pattern of life concept which simplifies the tourist behaviour models’ construction and usage. The digital pattern of life defines the general concepts of tourist behaviour, connects the tourist and the digital world and allows us to track behaviour changes over time. A literature review of the current state of the research in selected fields is performed for identifying the existing problems. The case studies of behaviour analysis based on classification, clustering and time series events behaviour models are shown. An ontological approach and artificial neural networks are used during behaviour model construction, training and evaluation. The gathered results can be used by smart tourism service developers and business stakeholders.

https://www.mdpi.com/1999-5903/12/10/165/pdf

Tourist Behaviour Analysis Based on Digital Pattern of Life—An Approach and Case Study

The tourism industry has grown rapidly in recent years, and IT technology is also having a big impact on tourists. Tourism services, information generated by tourists and other sources can be used to build models of tourist behavior. These models can improve the travel experience in various ways. The author presents the system for analyzing tourist behavior based on the concept of a digital pattern of life. The system determines the tourist, possible data sources, ways of storing and presenting data, as well as tools for analyzing behavior. The author used artifi cial neural networks to analyze behavior from a dataset of tourist travels made with cars. One scenario of tourist behavior using artifi cial neural networks is presented. The collected results will be used for improving tourist services.

Digital pattern of life based tourist behaviour analysis system

The trend of industry tourism GDP is increasing in every year that speculates from statistics of the World Travel & Tourism Council (2018). Moreover, travel industry not only considered as the most dynamic sector but also the most importance generator of income and jobs in the country. Thus, the prototype for tourism plans are needed for strategic planning. Currently, social web is a great tool for providing useful insights about tourist behaviors especially with the text data that comes from travelers' opinions. In this work, we use sequential patterns of users' behavior which are ordered by time from tourist including opinions, reviews as our input data. Then, we use Convolutional Long Short-Term Deep Learning (CLSTDL) which is a deep learning technique that combines Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) to predict the expected location. During the process, the output of CNN will be fed into LSTM to learn the sequence pattern behavior of traveler. The model output is then used to predict the next location that particular travelers are likely to go. The experimental results have shown that CLSTDL outperforms other models when evaluating with the accuracy and loss metrics.

Prediction of Tourist Behaviour: Tourist Visiting Places by Adapting Convolutional Long Short-Term Deep Learning

/pdf/prediction-sequence-patterns-of-tourist-from-the-tourism-lgadjd5f.pdf

Jaruwan Kanjanasupawan

Papers

Prediction of Tourist Behaviour: Tourist Visiting Places by Adapting Convolutional Long Short-Term Deep Learning

Prediction Sequence Patterns of Tourist from the Tourism Website by Hybrid Deep Learning Techniques