Analysis of driving behavioral data is recently developing and recognized as a necessary technique for driver assistance system. A driver model based on the analysis is used for understanding and predicting the behavior of drivers. In the present study, we propose dividing the driver model into two parts: a task model and a user model. The task model objectively expresses how driving tasks occur according to the geographic infromation and the traffic environment while the user model shows how the driver perceives the environment and makes decisions. We also show formulation and labeling of driving tasks based on formal grammars in the task model.

User modeling becomes more important at product making business because companies have to deal with a wide variety of customers favorites. We are researching customer behavior modeling based on customers' sensibility or "kansei". In this study, we focused on car selection as the first case of customer modeling and we researched factors related to the selection of a car through qualitative surveys. We took an Internet survey for car selection based on the result of past surveys. In this paper, we report the result obtained from an analysis of the survey data.

近年の知的情報処理やIoT 技術の急速な進展の結果，地域データを用いたスマートツーリズムサービス（STS) の開発が進んでいる．地域データとは，ユーザーである観光客が行動する地域で測定され，短時間のうちに利用されるデータを指す．STS は，地域データを知的情報処理技術で処理し，リアルタイムかつパーソナライズされた情報を提示するサービスである．多くの場合，地域でSTS を提供する事業者は地域データを大量に収集する能力が欠けているので，地域データを効率的に収集して地域としてSTS を戦略的に整備するためには，STS を地域での必要性に応じて優先付けし，優先度順に必要なデータを収集し，実装を進めることが望ましい．優先付けされたSTS のリストを，本研究ではツーリズムサービスポートフォリオ（TSP) と呼ぶ．地域データを収集・分析・分配する地域データプラットフォーム（RDP) を実装し，TSP に沿ってSTS を実装していくことにより，スマートツーリズムが実現できる．TSP とRDP を備えた観光地のことを，本研究ではスマートディスティネーションと呼ぶ．

In this paper, I defined event space as (1) a large number of exhibitors, (2) visitors freely traveling in the space, (3) holding exhibitions in a short period. In the event space, keeping in mind these issues : (a) The exhibitor burden is small. (b) The visitors can enjoy it. (c) The installation cost is low, and we have developed a visitor traveling data acquisition system using RF-ID. In the data acquisition method, visitors touch the card reader with the seal type RF-ID tag at the "touch point" in each exhibition and authenticate them. The developed system was experimented in actual event space for 4 days. In the experiment 62 exhibits participated as "touch point", and a total of 2706 visitors participated. Based on the acquired data, it was found that the number of visitor's visits greatly varies depending on the exhibition place. In addition, it was found that visitor traveling changes with the function that drinks are presented using the next generation vending machine according to the number of a visior's visits.

In Japan, there are many social problems caused by super-aged society. So we need to resolve these problems by using ICT. In addition, it is also necessary to actively use of big data in healthcare. In this study, we analyzed the elderly questionnaire to investigate relations between activity and subjective happiness level in this analysis. We used probabilistic Latent Semantic Analysis and Bayesian network. We found variables related to elderly heterogeneity and behavioral character. These findings make it possible to simulate elderly's activity and provide new insight into elderly's subjective level of happiness.

本研究では，カメラや各種センサを利用した高度な見守りサービス（以下，生活支援サービス）の利用を阻害する要因（以下，利用阻害要因）は利用者のパーソナリティに影響されると想定し，利用阻害要因とパーソナリティの関係性を分析した．具体的には，生活支援サービスのプロトタイプを作成し，育児世帯2世帯の実生活環境にセンサ機器を設置し2週間生活を行ってもらった後，デプスインタビューによって利用阻害要因を抽出した．その後，中学生以下の子を持つ親2,029名へのWebアンケートによって，生活支援サービスの利用意向及び利用阻害要因とパーソナリティの5 因子モデルとの関係性を調査をした．その結果，外向性，協調性の高い人は生活支援サービスの利用意向が高くなる一方，情緒安定性の高い人は利用意向が低くなること，情緒安定性と家族のプライバシーには負の相関が強いことなど，利用意向及び利用阻害要因は，利用者のパーソナリティに影響されることを明らかにした．

These days IDThese days ID-POS data are widely used with AI to understand consumers and improve retailing services. We suggest a way to make longitudinal research of ID-POS data to understand how and why any change in people's purchasing along with seasons with pLSA and visualized our result. Many people change what they buy in each season, but others don't in some seasons. So we investigated the reason why some people don't change their purchasing in some seasons with Bayesian network. The result told us that the seasonal purchasing changes are effected from consumers' family members and their preference. The result implicates that some changes in consumers' family members can cause some change of their purchasing behavior. Visualizing the result of AI's data analysis will help us understand what AI tell us and decide what we do to improve our business or lives.