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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_194.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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pheric pressure sensors are effective for identifying indoor floors that are outside the range of GPS radio waves. It also seems possible to construct, from such information, an automatic estimation model for behav- ioral contexts without forcing subjects to perform any action, by using a hidden Markov- type model. DATA- ORIENTED APPROACHES Such techniques that enable long- term online observa- tions of travel- activity patterns may also have a great influence on the usage of behavior models. Such moni- toring techniques will significantly affect the design of fares for public transportation, central urban area plan- ning, and transportation planning in real time. It is because the behavior databases, in which data continue to be accumulated through IT- based monitoring tech- niques, themselves will contain travel behavior models and activity models, making possible simultaneous searches for and viewing the findings on various travel behaviors. Both techniques for measuring travel behavior and computer techniques are ceaselessly progressing. It is impossible to create a program on a computer when the object to be calculated is not clear, and theoretical stud- ies, which allow calculation only with paper and pencil, have been considered superior in such cases. However, expectations are that enormous amounts of travel behav- ior data will continue to be stocked in databases as a result of progress in survey techniques, which has been shown in this paper. In that case, an effective analysis method will be data mining, which directly mines strate- gies effective for transportation policies from a large amount of data, unlike conventional approaches, which try to validate assumptions and reproducibility of mod- els by using data. A well- known application of data mining is the diapers- and- beer episode of Wal- Mart, the largest U.S. retailer. A correlation rule analysis, a typical method of data mining, was performed on an enormous amount of purchase data, which were being instantly collected through a point- of- sale system, and it revealed a rule that customers buying diapers on Friday evening tend to buy cans of beer as well. Wal- Mart immediately placed cans of beer beside the diaper section, and beer sales doubled. This episode indicates that it is possible to directly draw causal relationships of consumer behaviors, which are difficult to obtain by intuition of analyzers, from a large amount of data. This method is already in use for various business data analyses, such as those for inven- tory control, new product planning, securities valuation, stock valuation, and medical diagnosis, and it is deliver- ing remarkable results. Unlike conventional analysis approaches, which have poor flexibility in policy evaluation because of too much emphasis on consistency and reproducibility of models, data mining captures transportation policies in a mar- keting sense, on the basis of a large amount of data, and focuses on discovering effective relationships from the data. In fact, such a method based on data science is not considerably different from conventional approaches, which consist of the steps of data sampling, analysis, modeling, validation, and then solution of real problems. The only difference is that such a method tries to make data themselves reveal many relationships. Such a 194 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2 FIGURE 6 Measurement results of changes in acceleration at (a) coffee shop and (b) record shop. (a) (b) FIGURE 7 Ranges in atmospheric pressure.

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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