Abstract

Disruption of infrastructure services can cause significant social and economic losses, particularly in the event of a natural disaster. The World Bank Group and the Government of Japan established the Quality Infrastructure Investment Partnership to focus attention on the quality dimensions of infrastructure in developing countries, with a focus on promoting disaster resilience. Moreover, to support infrastructure investment decision making for sustainable and resilient development, the World Bank and Kyoto University have operationalized key resilience concepts at the project level and developed quantitative indicators capturing key aspects of infrastructure resilience related to the road transport sector. These indicators estimate resilience, expressed as functionality loss and recovery time across four dimensions: travel time, economic benefit, provision of life-saving services, and provision of relief goods. The paper applies indicator calculations to three case studies of proposed bypass roads in Japan and provides an example comparison of calculated indicators across the three projects for each resilience dimension. Further piloting of the approach will help refine the indicators, test their relative utility in decision making, and offer a better understanding of the data and analytical demands. Document type: Book

Abstract

Efficient human-machine networks require productive interaction between human and machine actors. In this study, we address how a strengthening of machine agency, for example through increasing levels of automation, affect the human actors of the networks. Findings from case studies within air traffic management, emergency management, and crowd evacuation are presented, shedding light on how automation may strengthen the agency of human actors in the network through responsibility sharing and task allocation, and serve as a needed prerequisite of innovation and change. Document type: Part of book or chapter of book

Abstract

Data is growing at an alarming rate. This growth is spurred by varied array of sources, such as embedded sensors, social media sites, video cameras, the quantified-self and the internet-of-things. This is changing our reliance on data for making decisions, or data analytics, from being mostly carried out by an individual and in limited settings to taking place while on-the-move and in the field of action. Unlocking value from data directs that it must be assessed from multiple dimensions. Data’s value can be primarily classified as “information,” “knowledge” or “wisdom”. Data analytics addresses such matters as what and why, as well as what will and what should be done. In recent days, data analytics is moving from being reserved for domain experts to becoming necessary for the end-user. However, data availability is both a pertinent issue and a great opportunity for global businesses. This paper presents recent examples from work in our research team on ubiquitous data analytics and open up to a discussion on key questions relating methodologies, tools and frameworks to improve ubiquitous data team effectiveness as well as the potential goals for a ubiquitous data process methodology. Finally, we give an outlook on the future of data analytics, suggesting a few research topics, applications, opportunities and challenges. This paper is based on a keynote speech to the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine on 16 May 2018.