Understanding consumer demand for new transport technologies and services, and implications for the future of mobility

The transport sector is witnessing unprecedented levels of disruption. Privately owned cars that operate on internal combustion engines have been the dominant modes of passenger transport for much of the last century. However, recent advances in transport technologies and services, such as the development of autonomous vehicles, the emergence of shared mobility services, and the commercialization of alternative fuel vehicle technologies, promise to revolutionise how humans travel. The implications are profound: some have predicted the end of private car dependent Western societies, others have portended greater suburbanization than has ever been observed before. If transport systems are to fulfil current and future needs of different subpopulations, and satisfy short and long-term societal objectives, it is imperative that we comprehend the many factors that shape individual behaviour. This chapter introduces the technologies and services most likely to disrupt prevailing practices in the transport sector. We review past studies that have examined current and future demand for these new technologies and services, and their likely short and long-term impacts on extant mobility patterns. We conclude with a summary of what these new technologies and services might mean for the future of mobility.Comment: 15 pages, 0 figures, book chapte

Construction of the Lung Cancer Phenotype Database (LCPD) for the German Center for Lung Research (DZL)

Anderton and Rowland's Terminator Miami - MM29 - photographed 18 August 1996

Comparing Carbon Performances of Mobility Services and Private Vehicles from a Life Cycle Perspective

Mobility services are predicted to replace private passenger vehicles to sizeable shares in the short- and middle-term. Although the carbon saving potential of mobility services compared to private vehicles is widely acknowledged, empirical studies are lacking and research designs remain unreplicated. In order to determine common characteristics of studies comparing life cycle carbon emissions of mobility services and passenger vehicles, we conducted a standardized literature review. We showed that current Life Cycle Assessment (LCA)-based approaches in the research field mostly apply two methodological characteristics: (1) person-km (p-km) are used as reference unit to compare carbon performances across transport modes and (2) scenario-analyses are used to deal with the poor data basis and disruptive character of mobility services. Most studies focus on comparing conventionally-powered car sharing vehicles to passenger cars within a one year timeframe in urban areas. Mobility services like ride hailing and pooling as well as alternative power trains remain largely neglected. Policy-makers and customers were found to be the main addressees of case studies. The private sector is least addressed thus showing the need for future research on a mix of mobility services and private vehicles with different power trains on fleet level

Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology

Research is increasingly becoming data-driven, and natural sciences are not an exception. In both biology and medicine, we are observing an exponential growth of structured data collections from experiments and population studies, enabling us to gain novel insights that would otherwise not be possible. However, these growing data sets pose a challenge for existing compute infrastructures since data is outgrowing limits within compute. In this work, we present the application of a novel approach, Memory-Driven Computing (MDC), in the life sciences. MDC proposes a data-centric approach that has been designed for growing data sizes and provides a composable infrastructure for changing workloads. In particular, we show how a typical pipeline for genomics data processing can be accelerated, and application modifications required to exploit this novel architecture. Furthermore, we demonstrate how the isolated evaluation of individual tasks misses significant overheads of typical pipelines in genomics data processing