Download or read book Artificial Intelligence Technology Predicts Travel Consumption Market written by Johnny Ch Lok and published by . This book was released on 2018-07-20 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has these two research questions need to be answered?(1) Can apply (AI) learning machine predict travelling consumer behavior?(2) Can (AI) big data gathering learning machine be replaced to human travelling marketing research method, e.g. survey or traveler psychological and travelling marketing research or travelling environment micro and macro economic human judgement of traveler consumption behavior prediction methods to predict travelling consumer behaviors more accurate?

Download or read book Learning Big Data Gathering to Predict Travel Industry Consumer Behavior written by Johnny Ch Lok and published by Independently Published. This book was released on 2018-10-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Challenges of artificial intelligence, algorithms technology and machine learning impact to consumption marketThe challenges of artificial intelligence, algorithms technology and machine learning impact to consumption market are similar to travelling entertainment consumption market. Markets have played a key role in providing individuals and businesses with the opportunity to gain from trade. If (AI) big data gather tool can predict how to change potential customer behavior in success. The challenges to consumers will face that the overall market consumption model will be dominated by the businessmen only. So, it is not fair or reasonable to consumers, because (AI) big data gather tool has controlled or dominated all consumers' minds and it has predicted how and why every kind of product or service consumer shopping model or consumption behaviors how will change.It will bring this questions: How can market designers learn the characteristics necessary to set optimal, or at least better, reserve prices after they had gather all data to conclude the analytical results of their consumers behaviors how will change? How can market designers better learn the environments of their markets?

Download or read book Artificial Intelligence Predicts Marketing Behavior written by Johnny Ch Lok and published by . This book was released on 2020-12-22 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. For example, Google's rank brain technology adds research by understanding the context in which the travelling consumer has entered it. Over time, rank brain will learn further from user behaviors Amazon's DSSTNE ( pronouned destiny) learns from shoppers' purchasing habits and consumption behavior to offer better product recommend actions, which Amazon can offer before a consumer has entered anything into the search bar. Such as (AI) big data can gather past online travelers' e-ticket purchase transactions to conclude that online traveler's travelling choice habits and online traveler consumption behavior to offer better travelling destinations and travelling package opinions to travel agents or airlines. However, this technology is not independent of human input. For example, Google engineers will periodically retain the rank brain system to improve the models it uses.

Download or read book Artificial Intelligence Big Data Travelling written by Johnny Ch LOK and published by . This book was released on 2018-06-17 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book indicates whether human technological AI (big data gathering tool) which can be applied to predict when, how and why consumer behavior will change. Does it is science story or actual fact to be applied in our future business society. Parent can learn their children to make judgement whether our future society will be either assistance by AI technological development absolutely or AI is only science story product. This book has these two research questions need to be answered?(1) Can apply (AI) learning machine predict travelling consumer behavior?(2) Can (AI) big data gathering learning machine be replaced to human travelling marketing research method, e.g. survey or traveler psychological and travelling marketing research or travelling environment micro and macro economic human judgement of traveler consumption behavior prediction methods to predict travelling consumer behaviors more accurate? 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 market 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?

Download or read book Is Artificial Intelligence The Best Traveler Behavior Prediction Tool written by John Lok and published by . This book was released on 2022-06-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Artificial Intelligent Future Development written by Johnny Ch Lok and published by . This book was released on 2019-07-25 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why does travelling market seem to similar to vehicle market which can apply (AI) learning tool to predict travellingconsumer behaviors?Artificial intelligence refers to complex in vehicle market and travelling entertainment market which is very seem to be applied to predict consumer behaviors.(AI) machine learning that posses the same characteristics of human intelligence and that have all our sense, all our reason and think just like human vehicle buyer who prefer vehicle purchase choice or travelling consumer who prefer travelling package or travelling destination and airline choice. Besides, machine learning is the practice of using algorithms to collect and examine data, learn from it, and then make a determination or prediction about something in the world. So, it can be attempted to gather data concerns that travelling consumer past travelling destination choice and air ticket price choice and different travelling package, e.g. high, middle, or low class hotel and foods supply and entertainment places choice in their past travelling journeys.The machine is " trained" using large amounts of data and algorithms that give it the ability to learn how to automatically perform a task with increasing accuracy. Otherwise, deep learning is primarily based on artificial neural networks inspired by our understanding of the biology of human's brains. Thus, (AI) big data can gather all these past traveler consumption behavioral choice data to make reference to analyze whether how many travelers will choose to go to the specific travelling destination in any time by the past traveler number record to different travelling destinations, then it can gather the past air ticket sale price to different destinations and past travelling package design to different destinations in order to analyze whether it is the cheap airline ticket price factor or attractive travelling package factor or attractive travelling entertainment etc. in order to predict which factor is the most potential influential factor to they choose to go to the destination to travel in different time within one year. Then, traveler agent or airline can collect these big data to judge how to design their package to attract travelers to go to anywhere to travel or what the main factor influence most of them to choose to visit the destination to travel.For example, travel agents or airlines can apply "Deep learning" breaks down tasks in ways that enables machines to assist them to predict when travelling consumer choice will be changed and why their travelling choice will change and how their travelling choice will change with increasingly complex tasks. So, such as why (AI) technology can be applied to predict how travelling consumer behavior changes to bring to judge whether anywhere will be many travelling consumers who will prefer to choose travelling hot destinations next year or next month. Then, travel agents and airlines can gather overall past travelling consumer data to analyze and conclude the more accurate prediction of different travelling destinations to the number of traveler. Then, they can choose how much air ticket price is more reasonable to charge to the travelling destination or how to design the travelling package which can bring more attractive to the prediction number of different travelling destination travelers in order to achieve to raise the different travelling destination number next year. Thus, (AI) big data machine learning can help airlines or travel agents to solve how to design any attractive travelling package challenge.

Download or read book Artificial Intelligence Big Data Travelling Consumption Prediction written by Johnny Ch Lok and published by Createspace Independent Publishing Platform. This book was released on 2018-06-10 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Artificial Intelligent Travelling Behavioral Predictive Tool written by Johnny Ch Lok and published by . This book was released on 2019-06-02 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: So, I believe that (AI) big data gather every conclusion or result will be different, due to consumer's price and quality demand will often change to every kind of product or service supply in retail industry. So, how to shape (AI) big data gathering's analytical conclusion or result more clear. I shall recommend organizations need to build great understanding of and a stronger connection to increasingly empowered consumers before they plan and implement how to apply (AI) big data gather tool to predict consumer behavior as below: Firstly, (AI) is empowered by technology, the consumer is redefining value. The traditional measures of cost, choice and convenience are still relevant, but not control and experience are also important. Globally, consumers have access to more than 2 billion different products choice by a wide range of traditional competitors and dynamic new entrants, all experimenting with new business models and methods of client engagement. As choice increases, loyalty becomes more difficult familiarity and the consumer becomes more empowered. Businesses will have no choice and constantly innovate and disrupt themselves by meeting new technologies of high standards and expectations of consumers. So, (AI) data gather tool will need to follow different target group of consumers' needs to follow their different kinds of product design or style choice preferable to gather data in order to conclude the different target groups of consumer behavior to give opinion more clear and accurate to let businessmen to understand more clear how its customers' behavioral choice trend in the future half month, even to two years period.Secondly, businessmen need to adopt changing technologies rapidly. Technology will be the key driver of this retail industry. Industry participants will only success if they have a clear prediction to focus on how to using technology to increase the value added to consumers. They must, however, do so will I realistic assessment of their costs and benefits. Hence, (AI) big data gather technological tools will need to design to help them to gather data efficiently by these ways, such as the internet of things ( IOT), artificial intelligence (AI) machine learning, augmented reality (AR)/virtual reality (VR), digital traceability. So, future (AI) big data gather tool are predicted to be most influential customer behavioral positive emotion changing tool for retail, due to their widespread applications, ability to drive efficiencies and impact on labor in order to impact consumer behavior changing effort from negative emotion to positive.Thirdly, (AI) big data gather tool is an advanced data science of consumer behavior predictive tool. Businesses will have to bring the journey from simply collecting consumer data to using it to scale and systematize enhanced decision making across the entire value chain. When focused on their business goals, industry players should not lose sight of the impact that future capabilities and transformative business models may have on society.However, (AI) big data gather tool will encounter these challenges when any business plans and implements to apply it to predict consumer behavior in retail industry. The challenges include that as below:1.The high cost and difficulty of implementing new technologies . The (AI) big data gather tool needs capital and capabilities to be designed to implement to be applied to different retail industry users. so, expensive barriers to innovation, an organization and the skillsets of its people to support a new design of (AI) big data gather tool, highly digital technology may be required.

Download or read book Artificial Intelligence Predicts Traveller Behaviors written by Johnny Ch Lok and published by . This book was released on 2019-07-03 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: What methods can predict future travel behavioural consumptionHow to use qualitative of travel behavioural method to predict future travel consumption. I also suggest to use qualitative of travel behavioural method to predict future travel consumption. Methods such as focus groups interviews and participant observer techniques can be used with quantitative approaches on their own to fill the gaps left by quantitative techniques. These insights have contributed to the development of increasingly sophisticated models to forecast travel behavior and predict changes in behavior in response to change in the transportation system. First, survey methods restrict not only the question frame but the answer frame as well, anticipating the important issues and questions and the responses. However, these surveys methods are not well suited to exploratory areas of research where issues remain unidentified and the researched seek to answer the question "why?". Second, data collection methods using traditional travel diaries or telephone recruitment can under represent certain segments of the population, particularly the older persons with little education, minorities and the poor. Before the survey, focus group for example can be used to identify what socio-demographic variables to include in the survey, how best to structure the diary, even what incentives will be most effective in increasing the response rate. After the survey, focus, focus groups can be used to build explanations for the survey results to identify the "why" of the results as well as the implications. One Asia Pacific survey research result was made by tourism market investigation before. It indicated the travel in Asia Pacific market in the past, had often been undertaken in large groups through leisure package sold in bulk, or in large organized business groups, future travelers will be in smaller groups or alone, and for a much wider range of reasons. Significant new traveler segments, such as female business traveler. The small business traveler and the senior traveler, all of which have different aspirations and requirements from the travel experience. Moreover, Asia tourism market will start to exist behaviors in the adoption of newer technologies, a giving the traveler new ways to manage the travel experience, creating new behaviors. This with provide new opportunities for travel providers. The use of mobile devices, smartphones, tablets etc. and social media are the obvious findings to become an integral part of the travel experience. Thus, quality method can attempt to predict Asia Pacific tourism market development in the future. However, improving the predictive power of travel behavior models and to increase understanding travel behavior which lies in the use of panel data( repeated measures from the same individuals). Whereas, cross-sectional data only reveal inter-individual differences at one moment in time, panel data can reveal intra-individual changes over time. In effect, panel data are generally better suited to understand and predict ( changes in ) travel behavior. However, a substantial proportion was also observed to transition between very different activity/travel patterns over time, indicating that from one year to the next, many people renegotiated their activity/travel patterns.

Download or read book How Artificial Intelligence Predicts Traveller Behavior written by Johnny Ch Lok and published by . This book was released on 2020-10-11 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether AI can predict climate change to influence travelling behaviours.The flexibility of human travelling behavior is at least the result of one such mechanism, our ability to travel mentally in time and entertain potential future. Understanding of the impacts is holidays, particularly those involving travel. Using focus groups research to explores tourists' awareness of the impacts of travel own climate change, examines the extent to which climate change features in holiday travel decisions and identifies some of the barriers to the adoption of less carbon intensive tourism practices. The findings suggest many tourists don't consider climate change when planning their holidays. The failure of tourists to engage with the climate change to impact of holidays, combined with significant barriers to behavioral change, presents a considerable challenge in the tourism industry.Tourism is a highly energy intensive industry and has only recently attracted attention as an important contributions to climate change through greenhouse gas emissions. It has been estimated that tourism contributes 5% of global carbon dioxide emissions. There have been a number of potential changes proposed for reducing the impact of air travel on climate change. These include technological changes, market based changes and behavioral changes. However, the role that climate change plays in the holiday and travel decisions of global tourists. How the global tourists of the impacts travel has on climate change to establish the extent to which climate change, considerations features in holiday travel decision making processes and to investigate the major barriers to global tourists adopting less carbon intensive travel practices. Whether tourists will aware the impacts that their holidays and travel have on climate changes.When, it comes to understand indvidual 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. Whether 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. The transportation tool factor will be depend to extent on the distance to the destination. This can also be interpreted in a social perspective as an intention with the resources available where much international tourism is structured around flying. To

Download or read book Prediction Artificial Intelligent Travel Health Education Transportation Space Exploration Consumer Behavior written by Johnny Ch Lok and published by Independently Published. This book was released on 2018-09-04 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Artificial Intelligence Predicts Traveller Behavioral Tool written by Johnny Ch Lok and published by . This book was released on 2020-02-16 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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?

Download or read book Robots Artificial Intelligence and Service Automation in Travel Tourism and Hospitality written by Stanislav Ivanov and published by Emerald Group Publishing. This book was released on 2019-10-14 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using a combination of theoretical discussion and real-world case studies, this book focuses on current and future use of RAISA technologies in the tourism economy, including examples from the hotel, restaurant, travel agency, museum, and events industries.

Download or read book Information and Communication Technologies in Tourism 2021 written by Wolfgang Wörndl and published by Springer Nature. This book was released on 2021-01-11 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 28th Annual International eTourism Conference, which assembles the latest research presented at the ENTER21@yourplace virtual conference January 19–22, 2021. This book advances the current knowledge base of information and communication technologies and tourism in the areas of social media and sharing economy, technology including AI-driven technologies, research related to destination management and innovations, COVID-19 repercussions, and others. Readers will find a wealth of state-of-the-art insights, ideas, and case studies on how information and communication technologies can be applied in travel and tourism as we encounter new opportunities and challenges in an unpredictable world.

Download or read book Artificial Intelligent Data Gathering Tool Predicts Travel Industry Consumer Behavior written by Johnny Ch LOK and published by . This book was released on 2018-10-13 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: The challenges of (AI) big data gather shapingthe future of retail for consumer industriesThe future of retail for consumer industries' (AI) big data gather challenges are similar to future travelling industry's entertainment consumption challenges. Another challenge of (AI) big data gather is that how to shape the consumer behavior to let business owner to feel or know oe predict. It means that how it express it's conclusion or opinion for every consumer behavior after it had gather all big data in any data gather period, e.g. three months, half year or one year consumer shopping model data gather period.Because every kind of industry, consumers will continue to demand price and quality change , with a wide range of convenient fulfilment options among of different kinds of products or services supply. Overall, the (AI) big data gather procedure gives opinion concerns every time retail experience will become more exciting, simple and convenient, depending on the consumer's ever-changing needs. So, I believe that (AI) big data gather every conclusion or result will be different, due to consumer's price and quality demand will often change to every kind of product or service supply in retail industry. So, how to shape (AI) big data gathering's analytical conclusion or result more clear. I shall recommend organizations need to build great understanding of and a stronger connection to increasingly empowered consumers before they plan and implement how to apply (AI) big data gather tool to predict consumer behavior as below:Firstly, (AI) is empowered by technology, the consumer is redefining value. The traditional measures of cost, choice and convenience are still relevant, but not control and experience are also important. Globally, consumers have access to more than 2 billion different products choice by a wide range of traditional competitors and dynamic new entrants, all experimenting with new business models and methods of client engagement. As choice increases, loyalty becomes more difficult familiarity and the consumer becomes more empowered. Businesses will have no choice and constantly innovate and disrupt themselves by meeting new technologies of high standards and expectations of consumers. So, (AI) data gather tool will need to follow different target group of consumers' needs to follow their different kinds of product design or style choice preferable to gather data in order to conclude the different target groups of consumer behavior to give opinion more clear and accurate to let businessmen to understand more clear how its customers' behavioral choice trend in the future half month, even to two years period.Secondly, businessmen need to adopt changing technologies rapidly. Technology will be the key driver of this retail industry. Industry participants will only success if they have a clear prediction to focus on how to using technology to increase the value added to consumers. They must , however, do so will I realistic assessment of their costs and benefits. Hence, (AI) big data gather technological tools will need to design to help them to gather data efficiently by these ways, such as the internet of things ( IOT), artificial intelligence (AI) machine learning, augmented reality (AR)/virtual reality (VR), digital traceability. So, future (AI) big data gather tool are predicted to be most influential customer behavioral positive emotion changing tool for retail , due to their widespread applications , ability to drive efficiencies and impact on labor in order to impact consumer behavior changing effort from negative emotion to positive.

Download or read book Prediction Artificial Intelligent Travel Health Education Transportation Space Exploration Industry Development written by Johnny Ch Lok and published by Independently Published. This book was released on 2018-08-28 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book How Artificial Intelligence Measures Traveller Needs written by Johnny Ch Lok and published by . This book was released on 2019-11-21 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.