Artificial Intelligence Applications in Tourism

2011 ◽  
pp. 241-247 ◽  
Author(s):  
Carey Goh ◽  
Henry M.K. Mok ◽  
Rob Law
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Demand Forecasting ◽  
Tourism Industry ◽  
Tourism Demand ◽  
Investment Planning ◽  
Data Mining Techniques ◽  
Travel And Tourism ◽  
The Future ◽  
Multiplier Effects

The tourism industry has become one of the fastest growing industries in the world, with international tourism flows in year 2006 more than doubled since 1980. In terms of direct economic benefits, United Nations World Tourism Organization (UNWTO, 2007) estimated that the industry has generated US $735 billion through tourism in the year of 2006. Through multiplier effects, World Travel and Tourism Council (WTTC, 2007) estimated that tourism will generate economic activities worth of approximately US $5,390 billion in year 2007 (10.4% of world GDP). Owing to the important economic contribution by the tourism industry, researchers, policy makers, planners, and industrial practitioners have been trying to analyze and forecast tourism demand. The perishable nature of tourism products and services, the information-intensive nature of the tourism industry, and the long lead-time investment planning of equipment and infrastructures all render accurate forecasting of tourism demand necessary (Law, Mok, & Goh, 2007). Past studies have predominantly applied the well-developed econometric techniques to measure and predict the future market performance in terms of the number of tourist arrivals in a specific destination. In this chapter, we aim to present an overview of studies that have adopted artificial intelligence (AI) data-mining techniques in studying tourism demand forecasting. Our objective is to review and trace the evolution of such techniques employed in tourism demand studies since 1999, and based on our observations from the review, a discussion on the future direction of tourism research techniques and methods is then provided. Although the adoption of data mining techniques in tourism demand forecasting is still at its infancy stage, from the review, we identify certain research gaps, draw certain key observations, and discuss possible future research directions.

scholarly journals Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market

2020 ◽  
Vol 12 (8) ◽  
pp. 3243
Author(s):  
Giovanni De Luca ◽  
Monica Rosciano
Keyword(s):  
Demand Forecasting ◽  
Tourism Industry ◽  
Tourism Demand ◽  
Campania Region ◽  
Travel And Tourism ◽  
Tourist Destinations ◽  
High Quantiles ◽  
International Tourist ◽  
Long Time ◽  
Hotel Accommodation

Travel and tourism is an important economic activity in most countries around the world. In 2018, international tourist arrivals grew 5% to reach the 1.4 billion mark and at the same time export earnings generated by tourism have grown to USD 1.7 trillion. The rapid growth of the tourism industry has globally attracted the interest of researchers for a long time. The literature has tried to model tourism demand to analyze the effects of different factors and predict the future behavior of the demand. Forecasting of tourism demand is crucial not only for academia but for tourism industries too, especially in line with the principles of sustainable tourism. The hospitality branch is an important part of the tourism industry and accurate passenger flow forecasting is a key link in the governance of the resources of a destination or in revenue management systems. In this context, the paper studies the interdependence of tourism demand in one of the main Italian tourist destinations, the Campania region, using a quantile-on-quantile approach between overall and specific tourism demand. Data are represented by monthly arrivals and nights spent by residents and non-residents in hotels and complementary accommodations from January 2008 to December 2018. The results of the analysis show that the hotel-accommodation component of the tourism demand appears to be more vulnerable than extra-hotel accommodation component to the fluctuations of the overall tourism demand and this feature is more evident for the arrivals than for nights spent. Moreover, the dependence on high quantiles suggests strategy of diversification or market segmentation to avoid overtourism phenomena and/or carrying capacity problems. Conversely, dependence on low quantiles suggests the use of push strategies to stimulate tourism demand. Finally, the results suggest that it could be very useful if the stakeholders of the tourism sector in Campania focused their attention on the collaboration theory.

Artificial intelligence (AI) for tourism: an European-based study on successful AI tourism start-ups

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Raffaele Filieri ◽  
Elettra D’Amico ◽  
Alessandro Destefanis ◽  
Emilio Paolucci ◽  
Elisabetta Raguseo
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Radical Change ◽  
Tourism Industry ◽  
Venture Capitalists ◽  
Content Type ◽  
Travel And Tourism ◽  
Study Experience ◽  
And Mathematics ◽  
Start Ups

Purpose The travel and tourism industry (TTI) could benefit the most from artificial intelligence (AI), which could reshape this industry. This study aims to explore the characteristics of tourism AI start-ups, the AI technological domains financed by Venture Capitalists (VCs), and the phases of the supply chain where the AI domains are in high demand. Design/methodology/approach This study developed a database of the European AI start-ups operating in the TTI from the Crunchbase database (2005–2020). The authors used start-ups as the unit of analysis as they often foster radical change. The authors complemented quantitative and qualitative methods. Findings AI start-ups have been mainly created by male Science, Technology, Engineering and Mathematics graduates between 2015 and 2017. The number of founders and previous study experience in non-start-up companies was positively related to securing a higher amount of funding. European AI start-ups are concentrated in the capital town of major tourism destinations (France, UK and Spain). The AI technological domains that received more funding from VCs were Learning, Communication and Services (i.e. big data, machine learning and natural language processing), indicating a strong interest in AI solutions enabling marketing automation, segmentation and customisation. Furthermore, VC-backed AI solutions focus on the pre-trip and post-trip. Originality/value To the best of the authors’ knowledge, this is the first study focussing on digital entrepreneurship, specifically VC-backed AI start-ups operating in the TTI. The authors apply, for the first time, a mixed-method approach in the study of tourism entrepreneurship.

Tourism Demand Forecasting Using Nonadditive Forecast Combinations

2021 ◽  
pp. 109634802110478
Author(s):  
Yi-Chung Hu ◽  
Geng Wu ◽  
Peng Jiang
Keyword(s):  
Prediction Models ◽  
Demand Forecasting ◽  
Combination Method ◽  
Fuzzy Integral ◽  
Tourism Demand ◽  
Grey Prediction ◽  
Investment Planning ◽  
Single Model ◽  
Combining Forecasts ◽  
Forecast Combinations

Accurately forecasting the demand for tourism can help governments formulate industrial policies and guide the business sector in investment planning. Combining forecasts can improve the accuracy of forecasting the demand for tourism, but limited work has been devoted to developing such combinations. This article addresses two significant issues in this context. First, the linear combination is the commonly used method of combining tourism forecasts. However, additive techniques unreasonably ignore interactions among the inputs. Second, the available data often do not adhere to specific statistical assumptions. Grey prediction has thus drawn attention because it does not require that the data follow any statistical distribution. This study proposes a nonadditive combination method by using the fuzzy integral to integrate single-model forecasts obtained from individual grey prediction models. Using China and Taiwan tourism demand as empirical cases, the results show that the proposed method outperforms the other combined methods considered here.

The future of travel

2020 ◽  
Author(s):  
Dr Simon Hudson
Keyword(s):  
Health And Safety ◽  
Tourism Industry ◽  
Lessons Learned ◽  
Travel And Tourism ◽  
The Future ◽  
The Subject ◽  
Travel Industry ◽  
And Behavior ◽  
The Impact ◽  
Global Travel

Most experts would agree that recovery from the COVID-19 crisis will be slow (see Figure 6.2), in large part due to the impact that the crisis has had on the global travel and tourism industry (Romei, 2020). Until there is vaccine, the virus will influence nearly every sector of travel from transportation, destination and resorts, to the accommodations, attractions, events and restaurants. The first section of this chapter looks at the future for these different sectors, a future heavily influenced by technology and a heightened emphasis on health and safety. The second part of the chapter focuses on a theme that has been prevalent in this book – the need for adaptability or ‘COVID-aptability’. Consumer demands and behavior will be permanently altered by the pandemic, and all stakeholders in the travel industry will need to adapt. One part of adaptability is redesigning servicescapes – a necessity for many after the lockdown, and this is the subject of the penultimate section of the chapter. The conclusion looks at lessons learned from this crisis.

A Study of XML Models for Data Mining

Data Mining ◽  
2013 ◽  
pp. 1-27
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Data Representation ◽  
Data Models ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Future ◽  
Future Data

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.

scholarly journals The Survival of Travel and Tourism Industry amidst the Covid 19 Pandemic

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Sandhya H
Keyword(s):  
Hospitality Industry ◽  
Global Scale ◽  
Tourism Industry ◽  
Research Papers ◽  
Travel And Tourism ◽  
Literature Reviews ◽  
The World ◽  
The Future ◽  
Tourism Professionals ◽  
Travel Industry

The Covid 19 pandemic has impacted the world and almost brought the entire world to a complete standstill. Most of the industries have been affected adversely as countries across the world went on lockdowns and imposed restrictions on travel, trade, and businesses. The tourism, Aviation, and Hospitality industry are among the few that have been most affected due to the pandemic, as pleasure travel had almost ceased to zero and many countries worldwide had closed their borders restricting international tourists. In order to survive this, the travel industry players had to cut down their employees and their pay. Many tourism professionals have lost their jobs or have their jobs at stake. This paper focuses on analyzing the overall impacts of the pandemic on the travel and tourism industry of India. The study aims at understanding the challenges faced by the different players in the tourism industry to survive the pandemic. The study also sheds light on the opportunities that await in the future on a post-Covid scenario and some of the methods adopted by the industry players to manage the future demand in the most sustainable and safe manner. The paper is conceptual and purely based on literature reviews of various research papers focusing on the Covid pandemic on a global scale. 

A Study of XML Models for Data Mining

2011 ◽  
pp. 1-28
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Data Representation ◽  
Data Models ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Future ◽  
Future Data

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.

Rural Entrepreneurship Through Tourism in the Himalayan Region With Special Reference to Arunachal Pradesh, India

2020 ◽  
pp. 194-207
Author(s):  
Debasish Batabyal ◽  
Bani Ratna Padhi
Keyword(s):  
Special Reference ◽  
Rural Economy ◽  
Global Economy ◽  
Service Sector ◽  
Tourism Industry ◽  
Arunachal Pradesh ◽  
Travel And Tourism ◽  
Rural Economic Development ◽  
Multiplier Effects ◽  
Tourism And Hospitality

The global economy has been witnessing phenomenal change over a long period. The service sector has been dominating the manufacturing industries. That shows the mammoth contribution of the tourism industry in terms of Rupee value. The report has revealed that in 2016 the sector generated INR14.1 trillion (USD208.9 billion) that represents 9.6% of India's GDP. Tourism industry has been creating around 10% jobs in the country that offered 40.3 million jobs in 2016 which promoted India's ranking in 2nd position across the globe in terms of total employment supported by travel and tourism trade. India's travel and tourism sector has been one of the fastest growing amongst the G20. Therefore, the tourism and hospitality sector may poise as an emerging area for the rural economic development. With its multiplier effects, tourism can energize the rural economy. This chapter has attempted to explore the opportunities and challenges of tourism entrepreneurship in rural India with special reference to Arunachal Pradesh.

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