Big Data & AI in Tourism

The internet expansion, digitalization, and social media-based revolution of technology have reformed the conditions of the market for tourism organizations. Samara, Magnisalis, and Peristeras (2020) define in the current age, big data (BD) and artificial intelligence (AI) are recognized as disruptive aspects and researchers try to find their values and impacts on the tourism industry. In respect of tourism, people have progressively gotten rid of simple tourism requirements towards integrated tourism requirements. Tourism has develop an efficient means for individuals to increase their prospects and augment their mystical world. The e-commerce of tourism has industrialized rapidly after long-term innovation and exploration. Coupled with the age of big data beginning, modified tourism’s concept has progressively come in the life of people. Integrate tourism is described as the type of travel which is associated with the localities where it occur and in respect of practical, it has a pure connection with local products, actions, resources, service & manufacture industries and involved local community. This paper describes the knowledge in the applying techniques of artificial intelligence to the planning of integrated tourism. And also discuss the business value and impact of ICT that focuses on the application of AI and BD technology in tourism. Lisi and Esposito (2015) define the industry of tourism, as the service-exhaustive industry, that severely depends on high-quality service delivery to the traveler. With the distribution of ICT (Information and Communication Technology), in the industry of tourism, service delivery has skilled model alterations and shifted the structure of the industry. While the tourism business expands, we are witnessing a period of dramatic software evolution that allows access to a wealth of tourist-related data. These details could include information about lodging, food and beverage outlets, cultural heritage areas of interest, and so on, as well as reviews, ratings, and tourist-generated recommendations. However, with such a data explosion, the number of options available grows tremendously, as does the potential of information overload. As a result, it's become even more critical to understand tourists' requirements and behaviours in order to improve the overall tourist experience by providing the right service to the right user at the right time. As a result of the aforementioned facts, the "Smart Touris" concept was born, which may be defined as a step forward from traditional tourism. Based on, a Smart Tourism Destination's primary purpose is to provide tourists with a smart experience enhanced by personalisation, context awareness, real-time data, and technology mediation. While a Smart Tourism destination should be an innovative site where all tourists may have a better, more engaged, and higher-quality travel experience, it should also improve the quality of life for locals. A variety of obstacles arise while attempting to establish a "Smart Tourism" destination, such as how to tailor the content offered to a user, which are the most appropriate sources for data collecting, how this data should be extracted (implicitly or explicitly), privacy issues, and so on.

Aims

This research aims to deliver a brief review on service development enabled with technology in tourism. Integrated tourism has a double goal. For several requirements, interests, and necessities the purpose is to be stuck together in an integrated, fused strategic plan of tourism. For tourism, the aim is to be planned to be stuck in the economic and social life of the area and their societies and In tourism-related articles, a number of research endeavours, concepts, issues, and concerns have been examined. As interest in these fields grows, the desire to compile all of the knowledge acquired thus far in this newly developed sector into a single publication emerges, in order to provide a greater understanding of the topic and lay the groundwork for future study. We take up this challenge in this literature study, and after analysing a large number of articles focusing on the Smart Tourism sector, we seek to identify the most often utilised approaches/concepts, something that, to the best of our knowledge, has never been done before.

The research purpose is to express the first phase to a renewable manual for consultants that familiarizes BDAI and requirements to stunned related contests from a technological and business viewpoint. By the exponential evolution of data size and unstructured data group, the information of data that wants to be managed has significantly beat the capacity of outdated database software and tools to manage, store, collect, and analyze and several innovative knowledges for dispensation huge data volume.

Background

With the ICT development as the evolution sign to the telecommunication and information era in the new age, the outcome has been reform the daily life applies of individuals and the continuous information flow. Filieri (2021) defines innovative tools that have appeared to simplify the distinctive daily subsists of individuals globally by increasing the restrictions of data practice to extensive inhabitants and presenting the space as the novel data storage method. Amid these are machine learning, big data, AI, and IoT, as consistent subjects. As a straight significance, here is the advent of infinite data that could be performed in the world and among the space and world. innovative tool also carries renovations in emphasize the varying consumer requirements in the novel age.

Objective

This research objective is to encourage an innovative service culture for marking a cutout fact in the model of local expansion and guiding the region conversion to the paradigm of “smart territory” wherever the territory is planned for a multiplayer system to expand through a satisfactory digital and technological infrastructure and its assistances in handling the regional stakeholder’s knowledge assets.

Mariani (2019) defines the tourist fingerprint visiting a part in the specified period can being employed and anonymized to continuously improve the user reporting with the tourist selection with the same profile. To this objective, it is essential to chase the trajectories of tourists and citizens with the technologies of wireless communication and localization.

Structure of the Report

This research report comprises the six-chapter. In the current introduction chapter, define the view of study, aim & objective, background, and structure of the report. In the current chapter, (BD) and (AI) are recognized as disruptive aspects and researchers attempt to find their values and impacts on the industry of tourism.

Chapter 2 describes the literature review that makes the theoretical framework of the related work in the research context. This examines the work in the big data and AI application used in tourism planning.

Chapter 3 defines the research methodology for designing the practical work of the research topic.

Chapter 4 describes the data analysis or implementation of the practical work in the application of big data and AI. Firstly, analyze the previous guidance in the tourism industry and innovative tools and methods. After that, implementing these tools and methods in the big data and artificial intelligence application.

Chapter 5 describes the result of the study related to the research aim and objective and evaluation of the big data and artificial intelligence application in tourism planning.

Chapter 6 presents the conclusion of the research. It provides the summary of the research, defines limitations and suggestions of the research, and gives a recommendation for further development.

Literature review

The business value of data technologies in tourism

Samara, Magnisalis, and Peristeras (2020) define the tourism sector required affordable wearable and mobile devices, infrastructure balanced distribution, and independent location and time. Big data and AI are effective for the tourism area. The tourism industry through legal and standard procedures creates a large amount of data such as data used for hotel rooms and booking flights. Data is analyses that transform the data into useful information and tourist business for capturing this opportunity to exploit relevant technology. McKinsey'sreport defines the use of artificial intelligence across the 13 industries' value chains. AI is used by the telecommunications and high-tech industries with the financial and automotive industries. In the tourism sector, the introduction of technology the benefits of AI technologies not had been proven.

Data-driven paradigm in tourism

Filieri (2021) defines changing and fast-growing tourism define the distribution characteristics. The highly competitive environment pushes the player to focus on the experience of travelers and more attention is given to data and information. The hyper-scale platforms like Airbnb shake the traditional supplier-centered business models. The sharing economy model with Airbnb exploits adaptive responses, dynamic pricing, and client-centric operations. The traditional online and offline players blended by the new business model with the ICT advancements. In advertising and distribution games with the business, data created by Facebook and Google aggressively impact the tourism operations and players. A strong industry disruption indicates by analysis and scoping on the disruptive factors. In tourism five major disruptive factors are defined as:

Expectations and growth of consumers create the new traveler's expectations and new markets. Also, consumers are well-trained from the other sectors such as retail and entertainment that provide personalized services, inspirational shopping, and experience.
Mobile tourism is growing continuously that disrupting spending patterns and travel behavior and supporting content co-creation and experiential travel.
Big data and artificial intelligence with computing power allow intelligent merchandising and preferences analysis in real-time. Also disrupts the tourism existing networks and operations.
Current players' balance and the status quo will disrupt by the processing and data collection regulation setting rules.
Travel distribution and unpredictably consumer behavior disrupts by travel risk occur from politics and socio-economic factors.

Big data and Artificial intelligence in tourism

Leung(2019) defines new technology provides different facilities such as the exchange of information, connectivity, and storing a large amount of data that is also known as big data. The tourist sector also contains a large amount of data that required effective technology to store necessary information. The three primary sources such as transaction data, spatial-temporal data, and user-generated content generate a huge amount of tourist data. Internet is used to generate the transaction data, IoT generates the spatial-temporal data, and social media generate the user content data. Inanc–Demir, and Kozak(2019) define Big data with AI and machine learning as improved tourism that provides smart service delivery. AI provides the robot and customer relation management built on big data provide better customer service than any staff.

Mariani (2019) defines the (BDAI) values on tourism under the responsiveness and timing touch the perspective of customers and suppliers. In the tourism industry, the non-appearance of the leader of BDAI provides the differentiation opening in the competitive setting while the acceptance of AI provides a competitive benefit. According to customer view, tools of big data and AI provide the ultimate customization and maximize revenues and performance according to supplier view. At every pre and post level increased the travel experience and exploits the big data distribution. In the tourism industry, a data-driven industry interesting paradox is witnessed instead of limited tech-mentality. Big data and artificial intelligence are becoming more dynamic and used increasingly due to the multiplication of available data and powerful computers. In tourism using the BDAI, calculate the area of the tendency for the next 15 years define in below figure1.

Figure 1: Forecasting BDAI for the next 15 years

Personalized pricing in tourism

Vaidya and Dhote, (2022) define tourism, personalization marketing as also a strategy on an individual basis to convey more promotional content. This is performed through mechanized programmed algorithms and gathering the data. This strategy allows provide the content to the user with specific demographics and also have searching patterns and certain concerns. Artificial intelligence-powered travel provides analytical capabilities and intelligent reminders and provides improvements to travel arrangements. Customized and distinctive pricing based on constructive segmentation provided to individual customers. In the tourism sector, a critical feature is stimulated their loyalty and traveler experience magnifies by the adopted price technique. According to the intended lifestyle and time arranged the dynamic pricing and customized pricing. In the tourism sector, the applicability is expanding and increasing at noon and low at dawn. Artificial intelligence through price differentiation between high rate and low rates increased the importance of customized charges. The computation system matches with the dynamic rate that provides the travelers with the most suitable rate. Dynamic rate differentiation can collect intelligence related to competitors' provided facilities cost and browse the website. Artificially intelligent recommended systems provide customized deals that provide benefits to the customer.

The marketing compositions such as purchase and post-purchase decision, alternatives evaluation, problem recognition, information search influenced by the five-stage model. The external and internal prompt propelled demand acknowledged by the consumer at the problem recognition stage. The consumer with intention of fulfilling their requirements then moves to the success stage. The finding of the source includes an individual link with service or company, mass media, firm promotional advertisements, and individuals. This procedure depends on economic factors, circumstances, societal impact, and discrete features.

Big Data Technology Application Impact on Tourism Planning

Zhang, Guo, and Su, (2021) define improvement in the living material standard also increased the people demand for mystical culture. People's requirements changed from modest tourism requirement to integrated requirements. An efficient way that enriches the people's spiritual world and expands their horizons is tourism. Tourism is known as the first industry that used network technology. E-commerce tourism developed rapidly after innovation and long-term exploration. After the use of big data technology, the customized tourism concept entered into people's life. The large amount of data created on the internet and data produced by social networks overload the emergence of information. Lisi and Esposito (2015) define that useful and effective information required a lot of energy that increased the demand for an effective system that provides useful tourism information. In the e-commerce big data, the author investigate on the tourism of e-commerce impact on tradition tourism and also for the custom tourism development provide some directions and ideas. Author in the E-commerce big data research includes Bayesian estimation algorithms, support vector machine classification algorithms, random forest algorithms. In the e-commerce big data to analyze the e-commerce impact on customized tourism developed a research strategy with different algorithms and perform research experiments on the impact of tourism. The results experiments define after using big data technology travel customization services experience 79.84% of customers prefer to purchase the product again. The personalized travel search services provide to the user through different tourism big data characteristics for classification and data mining. The basic technical supports provided by big data technology at the same time for the development of customized tourism defines it provide effective customized services for users.

Big data and AI-based Hybrid Recommender System for Tourism

Fararni(2021) state development in communication, technology, and the internet, at all level such as tourist events, heritage, transport, restaurants, hotels, and activities produce a large amount of tourist data, especially Online Travel Agency development. However, these web search engines offered list to tourist that is overwhelming, and obtaining results create information noise that slows down the selection process. In trip planning to assist the tourist and provide the necessary information they required, many recommender systems are developed. The author in their research discusses the different recommendation techniques or systems used in the tourism field. The author also developed a conceptual framework and architecture grounded on an approach of hybrid recommendation for the tourism recommender system. The developed system used the hybridization approach that address the limitation of the existing y system and overcome each technique's shortcomings that were used alone and used their strength as an advantage and research the different subjects. Tourist preferences and a list of tourist attractions recommendations also follow for developing the effective system. A detailed program is designed as a trip planner for specific visit duration involves heterogeneous tourism resources. Advanced technologies like AI, IoT, machine learning, and big data technologies are also used to develop a recommender system that promotes tourism.AI and big data integration are the main axes for the developed recommender system implementation. The aim of using AI and big data technology is to create sentiments, opinions analysis, and hybrid recommendations based on a big data solution using machine and deep learning techniques. The developed hybrid architecture objective is to help the tourist to personalize trips and provide the most relevant items that improve the visitor experience. The tourist selected the relevant set of elements, the developed system combining these items, and plans an appropriate trip using the operational research technique.

Figure 2: Tourism recommender system

Big Data and AI Legal and Privacy issues in Smart Tourism

There are a variety of ways in which AI could impact the legal system, including problems about crime, such as culpability if an AI is used for criminal purposes, and the extent to which AI could facilitate illegal actions like as tracking down travellers. When a circumstance arises when and where AI is used to cause personal injury, such as in an accident with an autonomous vehicle, question arises for the legal risk of manipulation and the need to build in accountability, The protection of users' privacy is unquestionably a big concern in the Smart Tourism business. The literature study in this research area revealed that tourist-related data, such as geographic regions, academic and professional backgrounds, hobbies, preferences, likes, and opinions, is vast. This information can be voluntarily provided, observed, obtained through digital data, or even inferred, and it mostly relates to personal information. Even though the aforementioned data can be used to provide personalised technology-enabled experiences through smart apps and services, it also poses the possibility of creating accurate tourist profiles, which in turn raises data privacy concerns, based on that the preservation of user anonymity and the avoidance of the publication of private user information are the two main privacy goals. Given that all systems must comply with the EU General Data Protection Regulation (GDPR), which is one of the most significant developments in data privacy regulation in the last 20 years, a fundamental shift in how data is handled in Smart Tourism is also predicted. With data at the heart of all Smart Tourism systems, legal ramifications of failed data protection and how to mitigate potential hazards have yet to be studied. Blockchain is a promising technology that has the potential to change the way data is kept while also promoting transparency and security. One of the newest technologies is blockchain with a wide range of applications in most industries and in-house Finance, healthcare, education, and, of course, tourism are among the industries. As a result of this reality, a number of papers have emerged that aim to either highlight existing blockchain uses in the tourism industry or propose new frameworks for integrating blockchain into Smart Tourism systems. Authors in Ref., for example, identify the The importance of crowdsourcing in Smart Tourism systems is highlighted, as well as the drawbacks. Maintaining the quality of crowdsourced data is critical. As a result, they advocate the use of blockchain technology to ensure the trustworthiness of the system. the aforementioned data's thinness.

Accountibility of AI and Big Data in Tourism

Accountability assures that if an AI makes a mistake or causes harm to someone, someone may be held accountable, whether it's the AI's designer, developer, or the company that sold it. There must be a system for redress in the event of injury, so that victims are adequately compensated.

Algorithmic accountability and responsible AI are two notions that are gaining traction in the literature. Algorithmic accountability is the attribution of responsibility for damages caused by algorithmically based judgments that result in discriminatory or unfair outcomes. The adoption of self-driving vehicles is one area where accountability is going to be significant. Who should be held responsible in the event of an accident? A handful of catastrophic accidents using self-driving cars have already occurred. For example, in 2016, a Tesla Model S outfitted with radar and cameras mistook a nearby lorry for the sky, resulting in a fatal accident. In March 2018, a car employed in Uber's self-driving vehicle experiments in Arizona struck and killed a lady. Even if self-driving cars are safer than human-driven cars, incidents like this erode faith. Regulation is one approach to ensure responsibility. Technology is generally trusted, according to Winfield and Jirotka (2018), if it provides benefits and is safe and well-regulated. Their article claims that ethical governance, which consists of a collection of rules, procedures, and policies, is one of the most important aspects of developing AI trust.

Individual designers and the organisations in which they work must accept these norms of conduct so that ethical issues are dealt with in a principled manner as they emerge, rather than waiting until a problem arises and dealing with it haphazardly.

They take the example of airliners, which we trust because we know they are part of a highly regulated industry with a proven track record of safety. Commercial aeroplanes are so safe because of a combination of outstanding design and rigorous safety certification standards, as well as the fact that when things go wrong, there are robust and publicly visible air accident investigation mechanisms.

Research methodology

This paper offers a research methodology on the e-commerce of tourism impact on modified tourism in the Era of BD, with the associated theoretic research method, the algorithm of random forest, Bayesian estimation, and support vector machine classification which are utilized to modify e-commerce tourism in the Era of BD, and study trial on the influence of tourism. Also recognizing the value of practical and theoretical that BDAI carries in the sector of tourism, we focus on supporting consultants that present BDAI to stunned related contests from a technological and business viewpoint.

In specific, we have demonstrated an ontology domain for Integrated Tourism and established a tool of Data Extraction for inhabiting the ontology with robotically saved data from the Web. Correspondingly, we have described numerous Services of Semantic Web on ontology at top and expressed a tool of Machine Learning toward well adjust the automatic configuration of these facilities to the demand of the user.

Theoretical research methods

Field Trip method

To recognize the progress position of modified tourism in the EBD, this research conducts an on-site examination to study information related to the modified tourism on the websites of tourism, this research is conducted on the major platforms of tourism e-commerce and collect on-site relevant information. After that, introduce the attained data that is used for the research topic’s in-depth analysis.

Literature analysis method

Published material acquired by bibliography recovery and internet analysis from the massive amount of bibliography materials. The study from foreign and domestic technologies of big data, custom tourism, tourism websites, cloud platforms, tourism of e-commerce, and other associated literature to familiarize the data of e-commerce tourism. Impact summary and in-depth analysis provided theoretical direction for the research study. This research followed the approach of systematic literature review as the structured methodology in a duplicable manner. The following libraries were utilized for the topic searching:

Science direct
Springer
ACM digital library
Scopus
IFITT database

The research analysis classifies the means where BDAI generates value in the four parts as follows:

Project: allowing businesses to improve projects and estimate to antedate demand, enhance R&D and expand tracking.
Produce: It growing skill of companies to generate services and products at inferior cost and advanced value.
Promote: serving help contributions at an accurate worth, with the correct communication, and to the correct target clients.
Provide: permitting them to deliver personal, ironic, and appropriate experiences of the user.

Related algorithm

Clustering algorithm

Zhang, Guo, and Su, (2021) define the method based on the grid enumerating the object planetary into inadequate units, creating a structure of the grid. All operations of clustering are accomplished on this structure of the grid. The main benefit of the clustering method is its profligate speed of processing, and its dispensation time is self-regulating of several data objects and only associated with several units in each quantization space dimension.

Bayesian estimation algorithm

Bayesian network is the model of the probabilistic graph. When studying the structure of the Bayesian network, it is incredible to estimate all networks’ scores in the planetary. We can initially utilize the search algorithm to exploration for a rating function-based detailed conceivable space for the selection of structure. Generally utilized search algorithms comprise algorithms of greedy search, hill-climbing, and taboo search.

State-of-the-art of tourism

Ontology has played a decisive part in cracking the problem of interoperability. Using an ontology domain a system of mediator software efficiently ”‘translates”’ data of partners and permits them to interconnect automatically. The researcher exposed that technologies of the Semantic Web can be utilized for the applications of tourism to deliver beneficial information on graphics and text, along with producing a semantic explanation that is understandable through machines. And define how to arrange semantically augmented travel services of Web and how to achieve semantics over the registries of Web service. They also discourse the essential to using the semantics in determining services of Web and their registries over peer-to-peer knowledge. The researcher examines the usage of ontological annotation in the applications of tourism. They demonstrate Web content-based quantitative analysis about accommodations of Austria, that unfluctuating a faultless explanation of prevailing content of Web that does not agree with the Semantic Web vision to develop an instant realism for e-commerce tourism. Correspondingly, they are discourse the inferences of these discoveries for several categories of the applications of e-commerce that depend on the information mining from prevailing sources of Web and strain the status of the technology of Semantic Web Services for the Web of Semantic, as well as e-tourism One particularly noteworthy finding from their research is that e-tourism focuses on digital connections, such as connecting consumers with businesses, but Smart Tourism focuses on connecting the physical and digital worlds by leveraging social media, cloud computing, and IoT.

References

Fararni, K., Nafis, F., Aghoutane, B., Yahyaouy, A., Riffi, J. and Sabri, A., 2021. Hybrid recommender system for tourism based on big data and AI: A conceptual framework. Big Data Mining and Analytics, 4(1), pp.47-55.

Filieri, R., D’Amico, E., Destefanis, A., Paolucci, E. and Raguseo, E., 2021. Artificial intelligence (AI) for tourism: a European-based study on successful AI tourism start-ups. International Journal of Contemporary Hospitality Management, 33(11), pp.4099-4125.

Inanc–Demir, M. and Kozak, M., 2019. Big Data and Its Supporting Elements: Implications for Tourism and Hospitality Marketing. Big Data and Innovation in Tourism, Travel, and Hospitality, pp.213-223.

Leung, X., 2019. Technology-enabled service evolution in tourism: a perspective article. Tourism Review, 75(1), pp.279-282.

Lisi, F. and Esposito, F., 2015.An AI Application to Integrated Tourism Planning. Lecture Notes in Computer Science, pp.246-259.

Mariani, M., 2019. Big Data and analytics in tourism and hospitality: a perspective article. Tourism Review, 75(1), pp.299-303.

Samara, D., Magnisalis, I. and Peristeras, V., 2020. Artificial intelligence and big data in tourism: a systematic literature review. Journal of Hospitality and Tourism Technology, 11(2), pp.343-367.

Vaidya, P. and Dhote, T., 2022. ONLINE TRAVEL COMPANIES AND CONSUMER ENGAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCEPrachiVaidya. [ebook] International Journal of Modern Agriculture, Volume 10, No.2, 2021. Available at: <http://www.modern-journals.com/index.php/ijma/article/view/734/630> [Accessed 10 March 2022].

Zhang, H., Guo, T., and Su, X., 2021.Application of Big Data Technology in the Impact of Tourism E-Commerce on Tourism Planning. Complexity, 2021, pp.1-10.

Big Data and AI in Tourism Planning: A Literature Review

Contents

Introduction