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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 this fourth part, This part brings readers to image what will be different if artificial intelligent non manual driving vehicle will be used to public transportation and private transportation both aspects in popular. I shall explain why AI safety system will be successful factor to influence future non-manual transportation successful development. Will it popular to accept to use any artificial intelligent vehicles? Is it possible to apply AI non-manual driving technology to AI non-manual driving transportation tools global transportation market? For example, in (AI) non-manual vehicle industry, driving automatic vehicle whether it will be accepted to drivers who have confidence to drive it on roads safely. Whether artificial (AI) intelligent non-manual driving systems are the improvement of traffic safety, reduction of energy consumption or improvement of the comfort of the driver. Whether will it be popular to accept to apply artificial intelligent non-manual driving technology from non-manual auto driving cars to be applied to any non-manual auto driving transportation tools transportation market development, such as train, tram, lorry, transportation air plane, passenger air plane, ferry, taxi, MTR. Etc. different kinds of transportation tools? If future human accepts to use any non-manual driving vehicles or non-manual driving transportation tools, what advantages and disadvantages will bring to influence our daily life. How if (AI) non-manual auto driving technology stage is mature to achieve non-manual driving technology is safe driving. It is possible that (AI) non-manual driving cars can influence to change whole manual driving transportation tools to non-manual driving transportation tools. How it will influence (AI) autonomous cars change to influence global manual driving transportation industry development ? To achieve non-manual driving industry development success. (AI) non-manual driving vehicle manufacturers need to ensure (AI) driving system is more safe to drive to compare manual driving on the road. If they expect non-manual driving transportation market development success. So, self improving systems are a promising new approach to developing artificial intelligence. But will their behavior be predictable? Can will be sure that they will behave as we intended even after many generations of self improvement? This part can present a framework for answering any questions concern whether future non-manual driving transportation market will be possible success. In fifth part, I shall explain how to apply (AI) tool to attempt to predict consumer behavior in travelling industry, this part 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.
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.
With AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.
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?
For another example, in 2016 year , Apple computer revamped its travelling scene photos app to allow travelling consumers to search for specific travelling destinations in the travelling scene phots, they want to find anywhere travelling destination photos, not just dates and locations. Each travelling photo that an intelligent phone or intelligent pad user takes goes through 11 billion computations, so that travelling scene photos can understand exactly where is the travelling destination photography to let online travelling consumer to feel anywhere they plan to go to the location to travel. So, (AI) learning machine can make online travelling photos more attractive to influence potential travelers choose to the destination to travel after they see the travelling destination scene photos from internet.It seems that in future, (AI) machine learning will allow online travelling search to evolve even further. Search engineers will deliver refined recommendations to airlines' online traveler e-ticket search users and use less human input to predict travelling consumers' needs from internet channel. For IBM computer example, it indicated 90% of the data that exists today has been created in the last two years.This huge explosion of past traveler's e-ticket consumption data gives the opportunity to quickly spot and react to the latest trends, fashion and fads among its travelling clients and potential clients. This will allow airline or travel agent companies to better engage with younger travelling consumers, who gain influence access to the latest travelling destination and package trends.They associate with to help define who they are as individuals. Thus, travelling company brands have to identify and make use of them before travelling consumers move on, but the vast quantity of past e-ticket purchase dataavailable makes from internet channel. This a resource-intensive task. For next example, Lesara, a based online clothes store, uses this machine learning to inform its product decision often gathering information from internal and external sources.When its trends -spotting shoes. Lesara has a range of over 20 styles and sells hundreds of pairs a day. It focus on giving consumers, the very latest trends allow Lesara to develop on average of 50,000 new items each year. It compared to 11,000 old items each year. Thus, travelling agents or airlines can attempt to apply (AI) big data gathering method to gather all past e-ticket purchase data, concerns where they prefer to choose to go to the destinations to travel and what travelling packages are the most attractive to the travelers to choose to buy. It aims to help them to predict where future travelers will prefer to choose to go to travel or what travelling package they will prefer to choose to buy next year.
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.
The acclaimed author explores the greatest travel writing by literary adventurers from Freya Stark and James Baldwin to Nabokov and Hemmingway. Paul Theroux celebrates fifty years of wandering the globe with this meditative journey through the books that shaped him as a reader and traveler. Part philosophical guide, part miscellany, part reminiscence, The Tao of Travel enumerates “The Contents of Some Travelers’ Bags” and exposes “Writers Who Wrote about Places They Never Visited”; tracks extreme journeys in “Travel as an Ordeal” and highlights some of “Travelers’ Favorite Places.” Excerpts from the best of Theroux’s own work are interspersed with selections from travelers both familiar and unexpected, including J.R.R. Tolkien, Samuel Johnson, Eudora Welty, Evelyn Waugh, Isak Dinesen, Charles Dickens, Henry David Thoreau, Pico Iyer, Mark Twain, Anton Chekhov, Bruce Chatwin, John McPhee, Peter Matthiessen, Graham Greene, Paul Bowles, and many more.
"TRB's National Cooperative Highway Research Program (NCHRP) Report 765: Analytical Travel Forecasting Approaches for Project-Level Planning and Design describes methods, data sources, and procedures for producing travel forecasts for highway project-level analyses. This report provides an update to NCHRP Report 255: Highway Traffic Data for Urbanized Area Project Planning and Design. In addition to the report, Appendices A through I from the contractor's final report are available on CRP-CD-143. These appendices supplement this report by providing a substantial amount of companion data and information. The appendices also include the extended literature review, the detailed NCHRP Report 255 review, supplementary tables, a list of defined acronyms, and a glossary. Also included on CRP-CD-143 are spreadsheet demonstrations, and, for reference purposes, a tool developed by the North Carolina Department of Transportation to assess annual average daily traffic."--Publisher's description.
This book provides glimpses into contemporary research in information systems & technology, learning, artificial intelligence (AI), machine learning, and security and how it applies to the real world, but the ideas presented also span the domains of telehealth, computer vision, the role and use of mobile devices, brain–computer interfaces, virtual reality, language and image processing and big data analytics and applications. Great research arises from asking pertinent research questions. This book reveals some of the authors’ “beautiful questions” and how they develop the subsequent “what if” and “how” questions, offering readers food for thought and whetting their appetite for further research by the same authors.