Across Europe, we witness a rise in the availability of mobility, road information and urban transportation data, from real-time streams coming from sensors installed all across cities, to data coming from personal devices of citizens and workers. The exploitation of these data sources is expected to greatly enhance the quality of life and overall well-being of European citizens, and stimulate the economy by facilitating novel data-driven services and better informed infrastructure investments and policies. Citizens and workers would benefit from increased accuracy in the traffic information they need, and from efficient and flexible data-driven transport flows. Public administrations can take data-driven decisions for investment and policy changes, being able to effectively evaluate them.
QROWD's overall objective was the development of a socio-technical solution to cross-sectorial Big Data integration in a European urban Smart Transportation, supporting participation and feedback of various stakeholder groups to foster data-driven innovation and synchronized access to Big Data sources. To achieve it, QROWD will create the first hybrid architecture for Big Data integration and analytics that seamlessly combines the scalability and reliability of Big Data processing and social computation, across the entire Big Data Value Chain, by leveraging machine learning algorithms and crowdsourcing services, providing the ability of connecting to different crowds, and synchronizing the varying speed of human and machine computation.
QROWD delivered a platform that implemented the hybrid architecture, and successfully piloted it to (i) estimate modal split with a combination of machine learning analysis and feedback collected from participating citizens (ii) generate maps of bike racks and yellow parking spots with data collected by volunteers on the field, crowdworkers on top of street imagery, Volunteered Geographic Information databases, all combined using QROWD's own fusion and interlinking framework.
