観光スポットとルート推薦のためのユーザ適応型旅行プラン生成アルゴリズム

早大学位記番号:新8428早稲田大

Summarization from Multiple User Generated Videos in Geo-Space

Ph.DDOCTOR OF PHILOSOPH

Travel chains in urban public transportation: Identifying user needs, travel strategies, and travel information system improvements

The implementation of a functional public transportation network has many benefits for a city, among other things, a way of sustainable mobility. Today, urban areas face the challenge of keeping up with technological trends and encouraging mobility activities using public transportation. For this reason, it is important to understand public transportation user behavior and, consequently, the motives and challenges related to urban travel. Research in the field of urban transportation mainly focuses on systematic and network-related issues to improve the travel experience. However, examining urban travel from a user’s perspective is equally essential to improving a city’s transportation network. With the help of twenty participants, an extensive travel study in the urban area of Zurich took place. The research design consists of a three-step mixed method approach. Data on travel behavior, mobility preferences, and information needs are obtained. The data is explored using an advanced travel chain structure, revealing results in the context of individual travel phases. The results show that urban travel relies heavily on the information apps provide, especially when planning. This need is mainly bound to spatial and temporal properties, for which app elements such as maps, dynamic timetables, and real-time information are most valued. Furthermore, travel using public transportation is approached by evaluating suggested routes according to the journey’s duration, efficiency, and complexity. However, decisions are often based on familiarity with the general area or interchange points. Uncertainties during urban travel are mitigated by walking when suitable, avoiding complex interchanges, and monitoring all phases with the help of an app. User results also indicate no serious issues regarding the City of Zurich as a public transportation provider. Nonetheless, measures could include integrating crowdsourced and context-aware data to meet the demands of adaptive and accurate travel information needs. The broader implications of the thesis outcome support cities and transportation service providers in understanding travel behavior. Consequently, this insight enables them to address specific needs and thus encourage sustainable mobility

An intelligent destination recommendation system for tourists.

Choosing a tourist destination from the information available is one of the most complex tasks for tourists when making travel plans, both before and during their travel. With the development of a recommendation system, tourists can select, compare and make decisions almost instantly. This involves the construction of decision models, the ability to predict user preferences, and interpretation of the results. This research aims to develop a Destination Recommendation System (DRS) focusing on the study of machine-learning techniques to improve both technical and practical aspects in DRS. First, to design an effective DRS, an intensive literature review was carried out on published studies of recommendation systems in the tourism domain. Second, the thesis proposes a model-based DRS, involving a two-step filtering feature selection method to remove irrelevant and redundant features and a Decision Tree (DT) classifier to offer interpretability, transparency and efficiency to tourists when they make decisions. To support high scalability, the system is evaluated with a huge body of real-world data collected from a case-study city. Destination choice models were developed and evaluated. Experimental results show that our proposed model-based DRS achieves good performance and can provide personalised recommendations with regard to tourist destinations that are satisfactory to intended users of the system. Third, the thesis proposes an ensemble-based DRS using weight hybrid and cascade hybrid. Three classification algorithms, DT, Support Vector Machines (SVMs) and Multi- Layer Perceptrons (MLPs), were investigated. Experimental results show that the bagging ensemble of MLP classifiers achieved promising results, outperforming baseline learners and other combiners. Lastly, the thesis also proposes an Adaptive, Responsive, Interactive Model-based User Interface (ARIM-UI) for DRS that allows tourists to interact with the recommended results easily. The proposed interface provides adaptive, informative and responsive information to tourists and improves the level of the user experience of the proposed system

Personalized Navigation Instruments for Map User Interfaces

A map is a big multi-scale information space. The size of a computer display, however, is limited. Users of digital maps often need to repeatedly resize and reposition the map to seek information. These repeated and excess interactions mar the user experience, and create bottlenecks for efficient information processing. We introduce personalized navigation instruments, a class of navigation instruments that leverage personal important spatial entities (e.g., landmarks and routes) to tackle navigation challenges in map user interfaces. Specifically, we contribute the following three instruments, each of which embodies a novel research idea: 1) Personalized Compass (P-Compass) is a multi-needle compass that extends the concept of a conventional compass to help users establish a reference frame. P-Compass localizes an unknown reference point by visualizing its relationship with respect to landmarks. P-Compass leverages what a user knows to help them figure out what they do not know. 2) SpaceTokens are interactive map widgets that represent locations, and help users see and link locations rapidly. With SpaceTokens, users can use locations directly as controls to manipulate a map, or building blocks to link with other locations. SpaceTokens make locations first-class citizens of map interaction. 3) SpaceBar associates a simple linear scrollbar with a complex nonlinear route, thus facilitates efficient route comprehension and interaction. SpaceBar is akin to a scrollbar for a route. We prototyped these three instruments in a custom smartphone application, used the application regularly in daily life, and validated our design in two formal studies. While maps are the focus in this dissertation, our ideas need not be limited to maps. For example, we have prototyped P-Compass with Google Street View and a 3D virtual earth tour application. We conclude this dissertation with several directions for future work, such as AR/VR and personalized spatial information user interfaces involving sound, gestures, and speech

Motivational techniques that aid drivers to choose unselfish routes

指導教員：角　

Mobile Campus Tour Guide for UTP

In line with the advancement of current technology, popularity of mobile tour guide application for universities is growing rapidly among the universities outside of Malaysia. Local universities are currently trying to adapt to this new phenomena. Current method used in exploring places in campus is time consuming and not effective. Common workaround is to look for the signboards or ask around but there are cases where the direction given was wrong and confusing. The objective of this project is to develop an android mobile tour guide application, ‘UTP Campus Tour Guide’, for Universiti Teknologi PETRONAS that provide users with interactive map to help them in exploring UTP. A review of existing mobile tour guide applications reveals diverse design methods were used for developing these applications. The results of careful analysis of these applications informed the design of the proposed application. The application is developed using MIT App Inventor 2 as the platform. A customized map of UTP and specific routes from one location to another location was developed using Google Maps Engine and then tested continuously to ensure its functionality working flawlessly. Users are able to find their preferred destination easily and can have a self-guided tour on their own with the help of this mobile tour guide application. Potential target users for this system will be mainly new students, new staffs and visitors of UTP who are not familiar with places in UTP. This mobile application will act as a map navigator for them to find places in UTP easily

A Comparative Analysis of High-Speed Rail Station Development into Destination and Multi-Use Facilities: The Case of San Jose Diridon

As a burgeoning literature on high-speed rail development indicates, good station-area planning is a very important prerequisite for the eventual successful operation of a high-speed rail station; it can also trigger opportunities for economic development in the station area and the station-city. At the same time, “on the ground” experiences from international examples of high-speed rail stations can provide valuable lessons for the California high-speed rail system in general, and the San Jose Diridon station in particular. This study identifies and draws lessons from European HSR stations that share similarities across several criteria with the San Jose area context. From an initial consideration of twenty European HSR stations, the researchers chose five stations for in depth case studies: Euralille and Lyon Part Dieu in France, Rotterdam Centraal and Utrecht Centraal in the Netherlands, and Torino Porta Susa in Italy. Additionally, the study drew information from relevant local actors and stakeholders to better tailor recommendations to the particular California context.Through the undertaking of different research tasks–literature review, case studies of European railway stations, survey of existing station plans and other planning documents for the Diridon station, station area analysis, and interviews with station area planners and designers–the study compiles timely recommendations for the successful planning of the Diridon station and other stations along the California high-speed rail corridor

Techniques for improving routing by exploiting user input and behavior

University of Minnesota Ph.D. dissertation. October 2014. Major: Computer Science. Advisor: Loren Terveen. 1 computer file (PDF); xiii, 106 pages.This dissertation explores innovative techniques for improving the route finding process. Instead of focusing on improving the algorithm itself, I aim to improve the other factors that make the route finding experience better: personalization, map data, and presentation. I do so by making extensive use of user input (both explicit and implicit) and crowdsourcing strategies. This research uses Cyclopath, a geowiki for cyclists in the Twin Cities, MN, as a case study for the various techniques explored.The first challenge is the lack of personalization in route finding algorithms. Aside from start and end points, algorithms usually know very little about users. However, user preferences can greatly affect their ideal routes. I studied the use of community-shared tags that allow users to specify preferences for those tags instead of doing so for each individual road segment, allowing them to easily express preference for a large number of roads with little effort. Correlation between individual road segment ratings and ratings deduced from tag preferences was evidence of the utility of this technique for making personalization easier.The second challenge is missing data. The best routing algorithm is only as good as the map data underneath it. Unfortunately, maps are often incomplete. They might not have updates on the latest construction, might be missing roads in rural areas or might not include detailed information such as lanes, trails, and even shortcuts. I present an HMM-based map matching algorithm that uses GPS traces recorded by users to generate potential new road segments. Tests within Cyclopath confirmed the abundance of missing roads and the ability of this algorithm to detect them.Finally, I look at the issue of unnatural presentation of routes. The way computers relay route directions is very different from humans, who use landmarks most of the time. However, gathering useful landmarks can be difficult and is often limited to points of interest. In this research, I tested methods for crowdsourcing different types of landmarks. I show that POIs are not sufficient to represent landmarks and that there is no objective truth regarding which landmarks are more useful to users