(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Self-guided trails – a route to more responsible tourism?

In recent years, the tourism industry has faced criticism by turning its attention to the development of more responsible tourism. This is a form of tourism that is mindful of the diverse needs of host communities, local business and the visitor. This paper investigates the development of more responsible tourism from the perspective of a popular tourist product – the self-guided trail or route. The trail or route provides a themed and interpreted journey through the urban or rural landscape, creating links between sites, attractions and other tourism businesses by providing information and storytelling along the way. These products have a global appeal and are becoming more prolific. Through a literature review on trails, the paper analyses their key characteristics and the rationales of developers and concludes that trails have the potential to contribute to more responsible tourism development. The paper proposes a series of planning principles which are derived from the literature and examples of good practice therein which may assist trail developers in creating more responsible tourism routes and trails. The discussion concludes with a case study of a project in Cornwall in the south-west of the UK, where responsible planning has underpinned recent trail development

Distinguishing different types of city tourists through clustering and recursive logit models applied to Wi-Fi data

We discuss the possibilities to distinguish different types of tourists based on Wi-Fi sensor data. The data are obtained from 20 sensors employed in Higashiyama, Kyoto, which is an area highly frequented by tourists. We describe tourist-tours as a sequence of sensors at which they are observed. Based on these records a clustering approach is chosen where we select as clustering variables, among others, the degree of detours and the length of time they are observed. We find that we can distinguish groups of tourists that are visiting a number of sightseeing spots in a short time from others who walk through the area more leisurely and are likely enjoying souvenir shops and restaurants. For the main tourist clusters than a Recursive Logit approach is applied to model their route-choice based on path length and attractions en-route. We find that the estimated parameters reflect these group characteristics

Institute of Northern Engineering 2006 Annual Report

MESSAGE FROM THE DIRECTOR -- OVERVIEW AND MISSION -- CENTER PROFILES -- ORGANIZATION & CENTER LEADERS -- ARCTIC ENERGY TECHNOLOGY DEVELOPMENT LABORATORY -- ALASKA UNIVERSITY TRANSPORTATION CENTER -- MINERAL INDUSTRY RESEARCH LABORATORY -- PETROLEUM DEVELOPMENT LABORATORY -- WATER & ENVIRONMENTAL RESEARCH CENTER -- INE GENERAL RESEARCH -- ACCOMPLISHMENTS 2006 -- GOALS 2007 -- RESOURCES, FUNDING, AND EXPENDITURE

Generating Paths through Cultural Heritage Collections

Cultural heritage collections usually organise sets of items into exhibitions or guided tours. These items are often accompanied by text that describes the theme and topic of the exhibition and provides background context and details of connections with other items. The PATHS project brings the idea of guided tours to digital library collections where a tool to create virtual paths are used to assist with navigation and provide guides on particular subjects and topics. In this paper we characterise and analyse paths of items created by users of our online system. The analysis highlights that most users spend time selecting items relevant to their chosen topic, but few users took time to add background information to the paths. In order to address this, we conducted preliminary investigations to test whether Wikipedia can be used to automatically add background text for sequences of items. In the future we would like to explore the automatic creation of full paths

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance