Can Apply Artificial Intelligent Tourism Prediction Tool To Predict Traveller Behaviors

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Can Apply Artificial Intelligent Tourism Prediction Tool To

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Author : Johnny Ch Lok
language : en
Publisher: Independently Published
Release Date : 2019-07-02

Can Apply Artificial Intelligent Tourism Prediction Tool To written by Johnny Ch Lok and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-02 with categories.

It will bring this question: Will tourists aware the impacts that their holidays and travel have on climate changes to influence their travelling decision?When, it comes to understand individual traveler's behavioral change, wide range of conceptual theories have been developed, utilizing various social, psychological, subjective and objective variables in order to model travel consumption behavior. These theories of travel behavioral change operate at a number of different levels, including the individual level, the interpersonal level and community level. Whether pro-environmental behavior can be used to predict travel consumption behavior in a climate change. However, the question of what determines pro-environmental behavior in such a complex one that it can not be visualized through one single framework or diagram.Despite the potentially high risk scenario for the tourism industry and the global environment, the tourism and climate change ought have close relationship. However, (AI) big data tool can be applied to find what factors to influence the time of travelers' travelling choices. What are the important factors and variables which can limit tourism? e.g. money, time, family problem, extreme hot or cold weather change, air ticket price, journey attraction etc. variable factors.Mention of holidays and travel were deliberately avoided in the recruitment process, so as not to create a connection factor to influence traveler's individual mind. However, the dismissal of alternative transportation modes can be conceived as either a structural barrier, in the sense that flying is perhaps the only realistic option to reach long-haul holiday destination, or a perceived behavioral control barriers in that an individual perceives flying as the only option open to whom.

Can Apply Artificial Intelligent Tourism Prediction

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Author : Johnny Ch LOK
language : en
Publisher:
Release Date : 2018-07-08

Can Apply Artificial Intelligent Tourism Prediction written by Johnny Ch LOK and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-08 with categories.

Nowadays, many airline firms or travelling agents hope to apply different methods to predict travelling consumer behaviors in order to know what will be future next month, even next year travelling market destination choice and travelling package design preferable choice activities and travelling consumers travelling packages or travelling destination taste changes to help them to choose to implement what kinds of travelling marketing strategies or what are travelling packages or airline ticket prices more reasonable or more accurate range price level to attract travelers choose to the airline or travel agent to buy paper or e- ticket or help them to arrange travel package more attractive. Hence, if the travel agent or airline can apply the most suitable travelling consumer behavioral prediction method to predict how and the reasons why future travelling consumers' choice will be changed to influence their frequent travelling destination or travelling package choice. It will have more beneficial intangible advantages to compare the non-predictive travelling consumer behavioral variable changes travel agents or airlines, e.g. what will be the hot travel entertainment destinations and tangible advantages, what are the most suitable airline and hotel reasonable price range level to attract many travelers to choose to find the airline or travel agent to help them to buy air ticket or they ought know how to design their arrange travel package which will be accepted more popular for next or next year travelling customer's hot needs .Otherwise, if they applied the inaccurate traveler consumer behavioral prediction market research methods, e.g. survey, telephone questionnaire to predict how their consumers' behavioral changes. It will waste their time and money to attempt to make wrong travelling hot destinations and travelling package design to make unattractive travelling marketing strategy to cause travelling customer number to be reduced. In my this book first part, I concentrate on explain why artificial intelligence (AI) big data gathering tool will be one kind of good traveler consumer behavioral prediction tool to be chose to apply to predict traveler consumer consumption behavior concerns when and why and how their travelling behavior will change. I shall indicate some cases examples to give reasonable evidences to analyze whether (AI) big data gathering tool will be one kind suitable tool to be applied to predict when and how and why travelling consumer behavioral changes. If (AI) big data can be one kind tool to attempt to be applied to predict when and how and why travelling consumer behavioral changes. Will it make more accurate to compare other kinds of methods to predict travelling consumer behaviors, e.g. survey, telephone questionnaire? Does it have weaknesses to be applied to predict travelling consumer behaviors, instead of strengths? Can it be applied to predict travelling consumer behaviors depending on any situations or only some situations? Finally, I believe that any readers can find answers to answer above these questions in this book.In the second part, I shall explain whether it is possible to predict travel behavioural consumption from traditional tourism market research psychology view . In this part, I shall indicate what factors can influence travel behavioural consumption, such as climate changing, renting travel car tools choice, the country's risk and safety. Then I shall indicate what psychological factors can influence travel behavioural consumption, such as: push and pull psychological factor, expectation and motivation and attitude factor.

Artificial Intelligence Predicts Traveller Behavioral Tool

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Author : Johnny Ch Lok
language : en
Publisher:
Release Date : 2020-02-16

Artificial Intelligence Predicts Traveller Behavioral Tool written by Johnny Ch Lok and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-16 with categories.

How can ARTIFICIAL INTELLIGENT online tourism sale channel influence traveling consumption of behavior?Nowadays, internet is popular, it seems that booking air ticket behavior of using internet is predicted to influence overall tourism air tickets payment method. Tourism industry has grown in the previous several decades. Despite its global impact, questions related to better understanding of tourists and whose habits. Using online travel air ticket booking benefits include booking electronic air tickets can be made from entering any electronic travel agents websites in the short time and electronic travel ticket payers do not need leave home, who can pay visa card to pre booking any electronic travel ticket from online channel conveniently.3.5How can analyze activity based travel demand ? Nowadays, human are concerning the traffic congestion and air quality deterioration, the supply oriented focus of transportation planning has expanded to include how to manage travel demand within the available transportation supply. Consequently, there has been an increasing interest in travel demand management strategies, such as congestion pricing that attempts to change aggregate travel demand. The prediction aggregate level, long term travel demand to understanding disaggregate level ( i.e. individual levels ) behavioral responses to short term demand policies, such as ride sharing incentives, congestion pricing and employer based demand management schemes, alternate work schedules, telecommuting limitation of travel agent traditionally work nature shall influence oriented trip based travel modelling passenger travel demand indirectly.Finally, online travel purchase will be popular to influence the number of travel behavioral consumption nowadays. Any travel package products can be sold from websites to attract travelers to choose to pre-book air ticket for any trips conveniently. In the past ten years, the internet has become the predominant carrier of all types of information and transactions. Regarding travel decisions, internet has also become an important sales channels for the travel industry, because it is associated with comparably lower distribution and sales costs, but also because it adapts to high supply and demand dynamics in this industry. Consequently, the travel and tourism industry tries to increase the internet sale specific share of sales volumes. So, internet sale channel has changed travel consumption behavioral pattern and characteristics and travel experience. For example, Switzerland has one of the highest population-to-computer ratio in Europe. It is also one of the most highly internet penetrated countries in terms of use of the WWW on a day-to-day basis, with more than 75 percent of the population older than 14 years using the WWW daily ( ICT, 2005).The reason of booking online tourism may include: convenience, fast transaction, finding traveling package choice easily, more airline seats available. So, online booking tourism will influence the traditional tourism agents visiting of sales and air tickets and travelling package numbers to be decreased. Finally, the online booking tourism market shares will be expanded to more than traditional tourism agents visits sale market in the future one day. So, the travel agents who still use the traditional tourism visiting sale channel which ought raise whose features to compare to differ to online tourism sale channel if these traditional tourism agents want to keep competitive ability in tourism industry for long term.What is actively based patterns of urban population of travel behavioral prediction method?

Is Artificial Intelligence The Best Traveler Behavior Prediction Tool

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Author : John Lok
language : en
Publisher:
Release Date : 2022-06-27

Is Artificial Intelligence The Best Traveler Behavior Prediction Tool written by John Lok and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-27 with categories.

I write this book aim to let readers to judge whether it is possible to predict future travel behaviour from past travel behaviour for travel agents benefits as well as big data gathering technology can be applied to predict travel consumption behavior if travel agents can gather any past travel consumer data to predict future travel consumption behavior from AI ( big data gathering tool). This book is suitable to any readers who have interest to predict any individual or family or friend groups of travel target's psychological mind to design the different suitable travel packages to satisfy their needs from big data gathering tool prediction method in possible. This book researches how to apply big data gathering tool to predict future travel consumer behavior from past travel consumer data. This book first part aims to explain why and how future artificial intelligent technology ( big data gathering method) can be applied to assist businesses to predict why and when and how consumer behavior changes in entertainment industry, e.g. cruise travel and vehicle leisure activities. If AI, big data gathering tool can be applied to predict such as leisure market consumption behavior, it is possible that future big data gathering tool can be used to gather past travel consumer behavioral data in order to conclude more accurate information to predict future travel behavioral need changes.

Can Apply Artificial Intelligent Tourism Behavioral Prediction Tool

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Author : Johnny Ch Lok
language : en
Publisher:
Release Date : 2019-05-25

Can Apply Artificial Intelligent Tourism Behavioral Prediction Tool written by Johnny Ch Lok and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-25 with categories.

When (AI) big data gather past every country traveler number who chose to go to which countries to travel in order to judge where destinations will be the country travelers' travelling choice destinations in the future.The factors influence where is the traveler choice, include personal safety, scenic beauty, cultural interest, climate changing, transportation tools, friendliness of local people, price of trip, trip package service in hotels and restaurants, quality and variety of food and shopping facilities and services etc. needs. So, whose factors will influence where is the individual travel's choice. It seems every traveler whose choice of travel process, will include past behavior. e.g. travelling experience, travelling habit, then to choose the best seasoned travelling action to satisfy whose travel needs. This process is the individual traveler's psychological choice process, who must need time to gather information to compare concerning of different travel packages, destination scene, climate change, transportation tools available to the destination, air ticket price etc. these factors, then to judge where is the best right destination to travel in the right time. Hence, (AI) big data can gather past different countries' climate changing data, transportation tool changing data, destination scene environment changing etc. different data to give opinions to travelling businesses whether any country's these above factors will influence about how many traveler number will be increase or decrease in the future.2.3Why can expectation, motivation and attitude factor influence travelling behavior?

Artificial Intelligence Predicts Traveller Behaviors

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Author : Johnny Ch Lok
language : en
Publisher:
Release Date : 2019-09-06

Artificial Intelligence Predicts Traveller Behaviors written by Johnny Ch Lok and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-06 with categories.

Can apply (AI) big data gathering method predict senior age will be main travelling target? In the past, Germany government had established tourism survey analysis to analyze survey data in order to arrive at reliable conclusions on future trends in travel behavior. To aim to find how demographic change will influence the tourism market and how the industry can adapt to those changes. The travel analysis provided data on tourism consumer behavior, including attitudes, motives and intentions. Since, 1970 year, it is based on a random sample, representative for the population in private households aged 14 years or older. Then, a continuous high scientific standard combined with a national and international users makes the travel analysis a useful tool and reliable source for tourism industry and policy decisions. It aimed to gather statistical data. e.g. on the age structure and on demographic trends, quantitative and qualitative analysis with time series data from the travel analysis. It shows e.g. not only the future volume, quite different from today's seniors, or how who will travel of family holidays will change, e.g. single parents of low, but grandparents of growing significance for tourism. Demographic change is said to be one of the important drivers for new trends in consumer traveling change behavior in most European countries ( e.g. Lind 2001). Because the growing number of senior citizens in the European Union and other industrialized countries, such as the USA and Japan, looks to become one of the major marketing challenges for the tourism industry. United Nations statistics predict that the share of people being 60 age or older will grow dramatically in the coming future, and is expected to rise from 10 percent of the world population in 2000 year to more than 20 percent in 2050 year ( United Nations Population Division, 2001). From its statistic, some data showed that travel propensity increased throughout life until the age of about 50 years of age and was then kept stable until very late in life 75 age. The most important results is that the travel propensity when getting older is not going down between 65 and 75 age of course, the overall development of this variable is influenced by a lot of other factors which are responsible for quite a variation over time. It is now possible to suggest that the general pattern of travel propensity is one of the key indicators for holiday life cycle travel behavior, includes three stages. The growth stage tends to increase from early adult hood until 45 age old or when reaching some 80%. The next stage is stabilization from the ages of around 50 age, until 75 age old, starting with a lower increase. Finally, the decrease stage is a slight decrease occurs once people reach the more advanced age of 75 age to 85 age old ( Lohmann & Danielsson 2001). So, it seems Germany government tourism prediction to future travelers' behavior indicated these findings, such as on how future senior generations will travel, who had used survey data to examine the patterns of travel behavior of a generation getting older and applied the findings to draw conclusions on the future. Also, it predicted that on the future of family trips, family segmentation will be the travel behavior patterns in the future. These findings together with the statistical data on demographic change allowed for a better understanding of the coming tends in family holidays. It's aim developed in consumer behavior related to demographic change and predicted what will happen future of tourism one had to consider other influences and drivers as well, for example, trends on the supply side. e.g. low cost airlines or in travelling consumption behavior in general whether how the past may provide a key to predict travel patterns of senior citizens to the future.

Prediction Artificial Intelligent Travel Health Education Transportation Space Exploration Consumer Behavior

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Author : Johnny Ch Lok
language : en
Publisher: Independently Published
Release Date : 2018-09-04

Prediction Artificial Intelligent Travel Health Education Transportation Space Exploration Consumer Behavior written by Johnny Ch Lok and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-04 with Business & Economics categories.

Introduction I write this book aims to let readers to feel that future artificial intelligent (AI) technology whether it could be applied to which aspects to satisfy our life need. I shall concentrate on predicting (AI) technology can be applied to these several aspects: education, business, transportation, space tourism, medical health these five aspects. This is my opinion, it is not absolute true. However, I shall follow our nowadays (AI) technological development trend to explain how I predict how (AI) technology will be invented to applied to these five aspects to satisfy consumer individual and businessman individual both needs, even I shall explain how to (AI) tool to predict consumer behavior in psychological function successfully. First part, I shall explain how and why (AI) technology can be applied to space exploration missions and space tourism development. Will artificial intelligent space exploration bring long term economic, entertainment and technological benefits? What factors will apply (AI) technology to assist space tourism leisure development? How to apply (AI) technology to solve any challenges during space travelling boats fly to space to encounter sudden accident. This part concerns whether artificial intelligent technology can be used in future space development. Firstly, I shall explain how human can apply artificial intelligent technology to space development which aspects. Then, I shall indicate how scientists need to follow what steps in order to achieve (AI) space robotic technology can be used in space technology to develop more successful. In this part, I shall also explain what benefits or strengths that (AI) space robotic technology can bring to assist space development as well as what disadvantages or weaknesses if (AI) space robotic technology can not be used to assist space development. I shall conclude whether (AI) space robotic technology will be real one tool which can assist space development, when human chooses to adapt to work and live and space tourism entertainment activities with (AI) space robots together in our future (AI) space robotic technological societies. Second part, I shall explain how future (AI) technology can be applied to business and health service aspect. The research questions: Can AI grow productivity? If AI can grow productivity, how can it raise ? If productivity raised, can it raise economic development ? How will (AI) influence human job change? Advances in artificial intelligence (AI) technology is for the progress in critical areas, such as health, education, energy, economy inclusion, social welfare and the environment. Thus, it brings this question: Which (AI) workers be instead of traditional human workers in these different new markets? In recent years, machines had been used to be human's tasks in the performance of certain tasks related to intelligence, such as aspects of image recognition. Experts also forecast that rapid progress in the field of specialized artificial intelligence will continue. Then, it also brings this question: Does (AI) exceed that of human performance on more and more tasks? If it is truth, will some of human jobs to be disappeared? (AI) will be instead of human some simple jobs, then unemployment rate to the low skillful and low educated workers will be increased.

Artificial Intelligent Traveller Emotion Prediction Tool

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Author : Johnny Ch Lok
language : en
Publisher:
Release Date : 2020-04-27

Artificial Intelligent Traveller Emotion Prediction Tool written by Johnny Ch Lok and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-27 with categories.

How can apply (AI) to provide travelling businesses with better-informed decisions ?I shall explain how (AI) big data gathering technology can provide travelling businesses with better-informed decisions to drive top-line growth, deliver meaningful experience for travelling customers and smooth their path along the travelling consumer journey. The widely understood definition of (AI) involves the ability of machines or computers to learn human thinking, reasoning and decision-making abilities. So, such as (AI) learning machine system can attempt to learn travelling consumer's travel destination or travel package thinking, judgement of their reasons why they choose to go to the destination to travel or why they choose to buy the travel package and learn how and why they make their past travelling decisions from their past travel big data gathering.A Narrative science study in 2015 year identified that (AI) was being used primarily in voice recognition, machine learning virtual assistants and decision support. This study also highlighted the many branches of (AI) and that techniques and their definition are used interchangeably. It is possible that (AI) can be used to gather big data, then to analyze to help travel businesses to predict travelling consumer travel destination and travel package choice behaviors. For example, one of the most common techniques is traveler machine learning, where algorithms are used to perform tasks by learning from the airline or travel agent whose past all travelers' travelling destination choice and travel package choice historical data. However, during 2017 year, search engines will begin to find what additional factors can influence past traveler personal travelling destination and travelling package travelling behavioral data into prediction of future travelling customer behavioral results, such as the online traveler (user's) history of travelling data searches, such as anywhere are the most popular travelling locations or travelling destinations and previously captures conservations. Artificial intelligence will use this past travelling destinations and travelling package information to power predictive search results, e.g. predictive future travelling consumer's choice behavioral processing for where will be their preferable travelling destination choice and how to design travelling package to satisfy future travelling clients' needs.Predictive search will improve the quality of online travelling search results, and provide new insights into travelling consumers' travelling destination and package behavior and the moments which matter to them. Search will give recommendation into tailored how travelling consumer individual travelling destination choice in travelling decision making process. Several of the largest online platforms already use (AI) travelling machine learning to improve predictive travelling consumer behavioral search results.

Artificial Intelligence Big Data Travelling Consumption Prediction Story

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Author : Johnny Ch Lok
language : en
Publisher: Independently Published
Release Date : 2019-03-08

Artificial Intelligence Big Data Travelling Consumption Prediction Story written by Johnny Ch Lok and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-08 with Business & Economics categories.

Future travel consumption behaviorCan (AI) big data gathering tool predict traveller individual habitual behaviour, e.g. renting travel transportation tools ?Can (AI) big data gathering tool can predict past traveller destination and travelling package choice habit and it can be intended to predict of future traveller behavior to people are creatures of habits judgement of future anywhere travelling destination choice next year or next month or next half year destination prediction ? Many of human's everyday goal-directed behaviors are performed in a habitual fashion, the transportation made and route one takes to work, one's choice of breakfast. Habits are formed when using the some behavior frequently and a similar consistency in a similar context for the some purpose whether the individual past travel consumption model will be caused a habit to whom. e.g. choosing whom travel agent to buy air ticket or traveling package; choosing the same or similar countries' destinations to go to travel; choosing the business class or normal (general) class of quality airlines to catch planes. Does habitual rent traveling car tools use not lead to more resistance to change of travel mode? It has been argued that past behavior is the best predictor of future behavior to travel consumption. If individual traveler's past consumption behavior was always reasoned, then frequency of prior travel consumption behavior should only have an indirect link to the individual traveler's behavior. It seems that renting travel car tools to use is a habit example. So, a strong rent traveling car tools useful habit makes traveling mode choice. People with a strong renting of traveling car tools of habit should have low motivation to attend to gather any information about public transportation in their choice of travelling country for individual or family or friends members during their traveling journeys. Even when persuasive communication changes the traveler whose attitudes and intention, in the case of individual traveler or family travelers with a strong renting travel car tools habit. It is difficult to change whose travel behaviors to choose to catch public transportation in whose any trips in any countries. However, understanding of travel behavior and the reasons for choosing one mode of transportation over another. The arguments for rent traveling car tools to use, including convenience, speed, comfort and individual freedom and well known. Increasingly, psychological factors include such as, perceptions, identity, social norms and habit are being used to understand travel mode choice. Whether how many travel consumers will choose to rent traveling car tools during their trips in any countries. It is difficult to estimate the numbers. As the average level of renting travel car tools of dependence or attitudes to certain travel package policies from travel agents. Instead different people must be treated in different ways because who are motivated in different ways and who are motivated by different travel package policies ways from travel agents.In conclusion, the factors influence whose traveler's individual traveller destination choice behavior The factors include either who chooses to rent traveling car tools or who chooses to catch public transportation when who individual goes to travel in alone trip or family trip. It include influence mode choice factors, such as social psychology factor and marketing on segmentation factor both to influence whose transportation choice of behavior in whose trip. So, (AI) big data can be attempted to gather past traveller transportatin tool choice, rent travelling car tools choice or catching public transportation tools choice to predict where destinaton can provide what kind of transportation tool to attract many travellers to choose to go to the place to travel.

Artificial Intelligence Big Data Travelling Consumption Prediction

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Author : Johnny Ch Lok
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-06-10

Artificial Intelligence Big Data Travelling Consumption Prediction written by Johnny Ch Lok and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-10 with categories.

Nowadays, many airline firms or travelling agents hope to apply different methods to predict travelling consumer behaviors in order to know what will be future next month, even next year travelling market destination choice and travelling package design preferable choice activities and travelling consumers travelling packages or travelling destination taste changes to help them to choose to implement what kinds of travelling marketing strategies or what are travelling packages or airline ticket prices more reasonable or more accurate range price level to attract travelers choose to the airline or travel agent to buy paper or e- ticket or help them to arrange travel package more attractive. Hence, if the travel agent or airline can apply the most suitable travelling consumer behavioral prediction method to predict how and the reasons why future travelling consumers' choice will be changed to influence their frequent travelling destination or travelling package choice. It will have more beneficial intangible advantages to compare the non-predictive travelling consumer behavioral variable changes travel agents or airlines, e.g. what will be the hot travel entertainment destinations and tangible advantages, what are the most suitable airline and hotel reasonable price range level to attract many travelers to choose to find the airline or travel agent to help them to buy air ticket or they ought know how to design their arrange travel package which will be accepted more popular for next or next year travelling customer's hot needs .Otherwise, if they applied the inaccurate traveler consumer behavioral prediction market research methods, e.g. survey, telephone questionnaire to predict how their consumers' behavioral changes. It will waste their time and money to attempt to make wrong travelling hot destinations and travelling package design to make unattractive travelling marketing strategy to cause travelling customer number to be reduced. In my this book, I concentrate on explain why artificial intelligence (AI) big data gathering tool will be one kind of good traveler consumer behavioral prediction tool to be chose to apply to predict traveler consumer consumption behavior concerns when and why and how their travelling behavior will change. I shall indicate some cases examples to give reasonable evidences to analyze whether (AI) big data gathering tool will be one kind suitable tool to be applied to predict when and how and why travelling consumer behavioral changes. If (AI) big data can be one kind tool to attempt to be applied to predict when and how and why travelling consumer behavioral changes. Will it make more accurate to compare other kinds of methods to predict travelling consumer behaviors, e.g. survey, telephone questionnaire? Does it have weaknesses to be applied to predict travelling consumer behaviors, instead of strengths? Can it be applied to predict travelling consumer behaviors depending on any situations or only some situations? Finally, I believe that any readers can find answers to answer above these questions in this book.