Data Market Austria (DMA) in der
9. Ausschreibung des FFG Programmes „Mobilität der Zukunft“
As most solutions build on proven web and storage-architectures, they do not scale to hundred- thousand or million sending events per second, which will be normal soon for mobile solutions for country, continent and world-wide business cases. So future mobile services must be grounded on an extreme scalable IT (software) infrastructure.
To achieve the enormous potential of connected mobility solutions for the general public, future services need prediction strategies. Typical preconditions for predictions are complex and therefore time-consuming algorithms and access to different datasets. With two demonstrator implementations we want to show how open data can be used together with closed data, how the same data can be used in different scenarios and business domains both for analysis and operations and how added value can be generated.
Taxi Fleet Management: Both public data offers, e.g. actual arrival and departure times of aeroplanes and trains, information on large cultural, sports events or congresses, and proprietary data, e.g. knowledge on the number of persons at places based on mobile phone usage, or weather prediction, can be used to implement prediction models and thus allowing the fleet operator an optimized planning of his capacities.
Historical Traffic Flow Characteristics: DMA will develop methods and tools that enable the extraction and provisioning of historical traffic flow characteristics and mobility patterns. The usage of the Data-Services Ecosystem enables unprecedented possibilities. Firstly, knowledge from multiple (open and closed) datasets can be combined to provide higher quality results (e.g., cellular network data aided by floating car data) or to study interdependency factors (e.g., impact of weather on mobility). Secondly, users can be offered the possibility to search, discover, and access (free or paid) mobility information in an understandable and ready-to-use form, rather than buying stand-alone complex raw dataset. Having access to knowledge instead of data, allows filling the gap between data provider and data consumer.