Tourism Informatics

Recently, people in local community often address tourism planning through their collaboration. The process of the collaborative tourism planning consists of planners’ learning and consensus building. Without the knowledge of the tourism planning, it is difficult for the planners to begin the collaborative tourism planning. Nowadays, the practical case reports of tourism planning are available online and include useful knowledge for tourism planning. So, we propose the support system for the collaborative tourism planning by using the case reports that are collected via Internet. Typical support systems support users’ retrieving the case reports based on the keywords that are inputted by the users. However, to input the keywords is hard for planners who are not familiar with tourism. Therefore, the proposed system extracts several topics together with keywords of the topics, which enables the planners to find appropriate case reports.

We are developing a recommender system for tourist spots. The challenge is mainly to characterize tourist spots whose features change dynamically with trends, events, season, and time of day. Our method uses a one-class support vector machine (OC-SVM) to detect the regions of substantial activity near target spots on the basis of tweets and photographs that have been explicitly geotagged. A tweet is regarded as explicitly geotagged if the text includes the name of a target spot. A photograph is regarded as explicitly geotagged if the title includes the name of a target spot. To characterize the tourist spots, we focus on geotagged tweets, which are rapidly increasing on the Web. The method takes unknown geotagged tweets originating in activity regions and maps these to target spots. In addition, the method extracts features of the tourist spots on the basis of the mapped tweets. Finally, we demonstrate the effectiveness of our method through qualitative analyses using real datasets on the Kyoto area.

Individual opinions and experiences are published in Web as CGM (consumer generated media). A tourism blog which a tourist wrote his experience and impression in a certain area is very helpful information for other tourists. However, a user cannot obtain such precious information without knowing the relation of blog articles and concrete place-names. We paid our attention to the hierarchical structure of place-names. In this paper, we propose the method of connecting related words to the place-name which does not appear explicitly in a blog article paying attention to the hierarchical structure of place-names. From 45,553 blog articles about the Karatsu area in Saga Prefecture, the potential related words about 78 place-names of Saga Prefecture which have not appeared in the blogs were extracted. 4 subjects evaluated that meaningful related words are obtained in 80 % or more of the place-names. However, the direct relationships between the place-name and related words was not able to be guessed easily.

Some tourists will write their activities to blogs. Such individual experiences are interesting compared with the official information by tourist agents. Therefore, extracting tourist’s activities from blogs are meaningful. We have attempted to extract tourism objects from tourists’ behavior statistically. In this paper, the objects of the behavior were extracted using dependency analysis, and tourism objects were specified by evaluation with regionality.

The industrial tourism has become increasingly common in recent years. The core of industrial tourism is industrial culture, which generatesexperiencesthatenable tourists to maximize their travel experiences. This study focused on the duck industry in Yilan and explored the local policy making that isbased on the cultural creativity. There are three models that govern the transformations of the traditional duck breeders with cultural creativity. They include (1) the conversion of industry-specific knowledge from an ecological protection perspective; (2) the development of experiential tourism from a cross-sector collaboration perspective; and (3) the expansion of the international market from a foreign development perspective. This study endeavored to explore the novel values and output values generated in the transformation of the duck breeding methods used in Yilan byanalyzingthe perspective, concepts, and practicesadopted for a rice-duck farming system in life, production, and ecology. To achieve this goal, literature review, field survey, and text analysis were conducted, and digital media such as microfilmsand mobile applications (APPs) were used. We found that microfilms conveyed the meaning of cultural creativity after the transformation of the duck breeding industryinto an industrial tourism-oriented industry and emphasized the life cycle, output, and ecological values of industrial tourism. In the future, by integrating microfilmswith quick-response code (QR Code) technology to enable tourists to browse using APPs not only established prior knowledge of the duck industry among tourists, but also elevated the innovation value and economic output of duck industry transformation by providing a rice-duck farming experience.

The Web contains much information for the tourism, such as impressions and sentiments about sightseeing areas. Analyzing the information is a significant task for tourism informatics. A useful target resource for the analysis is information on Twitter. However, all tweets with keywords, which are related to facilities and events for tourism, might not be tourism information. In this paper, we propose a method for estimating on-site likelihood of tweets. The task is to identify whether each tweet has high on-site likelihood or not. We introduce a filtering process and a machine learning technique for the task. In addition, we apply previous and next tweets for the identification task, as context information. Experimental results show the effectiveness of the combination method and context information.

In this chapter we present a method for interactive retrieval of information on accommodation facilities to support travelling customers in their travel preparations. The proposed method consists of four main functions. Firstly, the system procedure performs primary and secondary search. Secondly, the system obtains information on user interests through an interactive interface. Thirdly, it computes the suitability of accommodation information based on user interest. Finally, the accommodation information is visualized in order of suitability in lists and graphs. The results of an evaluation experiment showed that over 80 % of evaluators supported our system more than the baseline system. Therefore the principal functions of the proposed method are significantly effective.

Public transportation guidance services, such as Yahoo, Jorudan and NAVITIME, are widely used nowadays and support our daily lives. Although they provide useful services, they have not fully been personalized yet. This paper presents a personalized transportation system called PATRASH: Personalized Autonomous TRAnsportation recommendation System considering user context and History. In particular, we discuss an Adaptive User Interface (AUI) of PATRASH. Before designing a personalized route recommendation function for PATRASH’s AUI, we investigated possibilities and effectiveness of the function. First, we collected and analyzed 10 subjects’ usage histories of public transportation. Through this investigation, we confirmed the possibilities and effectiveness of the personalized route recommendation function. Second, we investigated the effectiveness of the basic functions of PATRASH’s AUI by comparing with two major transportation guidance systems in Japan. We evaluated those systems from the point of view of usabilities: click costs and time costs. The experimental results illustrate the effectiveness of AUI of PATRASH.

We have already proposed a sightseeing spot recommendation system based on information on the Web. An input for the prototype system was a user’s favorite location or facility. Our system computed a similarity measure between a target location that a user selects and each sightseeing spot in our database. One interesting feature for the similarity calculation in our system is time sequence information of each sightseeing spot. The prototype system used the number of hits in Yahoo Chiebukuro for the feature. We regard the time sequence as the potential-of-interest days for each sightseeing spot. In this paper, we focus another information resource for the time sequence feature; Panoramio. Panoramio is a geolocation-oriented photo sharing website and is useful to obtain the time sequence feature. Chiebukuro and Panoramio have different characteristics. Therefore, we compare the two information resources. We discuss the overall difference, the burst points and visualization. We also discuss several aspects of the time sequence which includes the merit and demerit of the feature.

The suspect vehicle detection system normally compares the list of criminal license plates and vehicle license plates gathering from various sensors in order to identify the criminal/suspect vehicles. However, the traditional process of comparing those license plates utilizing the matching of alphabet character is not effective. In traditional methods, the system unable to detect the criminal/suspect vehicles if the characters of the licence plate do not totally match with the blacklisted license plates. This paper proposes the use of reputation algorithm to detect the criminal/suspect vehicles that crossing the checkpoint which license plates match with the blacklist in the checkpoint database. In addition, we also use association analysis concept to detect the vehicles crossing the checkpoint that might relate to the criminal activity records. Our method can detect the suspect vehicles with forged license plate by using color, brand and type of the vehicles instead of only the license plate number matching method. These two techniques use a blacklist of criminal vehicles and criminal activity recorded in a criminal report database of Defence Technology Institute (DTI), Thailand, to help facilitate the detection process. From our extensive experiments, the results show that the reputation algorithm and the association analysis concept can improve the detection capability of the suspect vehicle detection system.