This crowdsourcing-enabled big data integration and analytics services platform allows the efficient management of the entire mobility/transport/logistics related data value chains: integration of existing (heterogeneous) data sources affecting mobility; Generation of new data; Curation and meaningful analysis and visualisation of actionable information. Under the hood, the platform integrates techniques, tools and components which are outstanding technological innovations. All in all, the QROWD platform offers a competitive range of solutions to key Smart Cities challenges, consistently delivering mobility services and analytics to support:
a) mobility policy formulation, monitoring and evaluation
b) traffic management in complex scenarios, and
c) engagement of citizens in building the future city, improving their sense of “ownership” of city innovation.
- Enables efficient integration of cross-sectoral big data, leveraging diverse sources and collection methods
- Enables the inclusion of humans in the data value chain loop by means of citizen engagement and crowdsourcing
- Orchestrates bespoke data flows with BDVA, BDE and FIWARE and Apache NiFi compliance
- Provides Big Data storage and semantics for data interoperability between components
Smart Cities, Urban mobility, Transport logistics
- New city challenges: Urbanisation - Migration – Ageing; Rising number and diversity of vehicles; Growing congestion; Rising quality of life expectations; Losing contact with the new citizens: Growing complexity of cities!
- 220+ Million Europeans live in cities in the process to become Smart Cities
- Mobility & transport are two of the most burning issues on the agenda of Smart Cities
- People have changed their travel, mobility and ownership of transport media preferences
- Transport accounts for 4% of EU’s GDP; drivers spend yearly tens of billions searching and overpaying for parking; traffic congestion costs Europe €100 B/ year and road transport is responsible for 40% of CO2 emissions
- Currently no standard for description of crowdsourcing tasks exist, each platform has its own mark-up language.
- Better informed policy decision making: City managers progress from problem solving to planned improvements
- Fit-for-purpose technology stack (leveraging existing technologies) ensures mobility management efficiency and ROI for Smart City initiatives: Modularity based on Dockers software containerization, Scalability via container orchestration and Large-scale data processing performance secured by Apache Sparkfast
- Extendable and scalable to adapt to diverse urban contexts
- The inclusion of the Human Factor in the data value chain loop can generate direct positive impact in terms of innovative and better ways of engaging and empowering citizens in defining their future city
- Contribution to trust build-up among local stakeholders: Enhanced visibility and transparency of city initiatives
- Add-on solution “Digital Secretary”, allowing the reuse of the QROWD platform for cognitive business robotics
- Hybrid human-machine algorithms to improve accuracy and reliability while managing execution time
- Human-in-the-loop approach/architecture to master sparse, messy data improves the accuracy of predictions
- Sustainable urban auditing through online and mobile crowdsourcing
- Value-centric approach to platform design: personal data empowerment, open source, building upon standards
- Knowledge graphs and linked data to turn raw data into useful, actionable data