QROWD · Project

Smart Platform That Merges City Traffic Data So You Can Actually Use It

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Imagine every city has dozens of data sources about traffic — bus GPS, road sensors, weather feeds, news reports — but none of them talk to each other. It's like having puzzle pieces from different boxes. QROWD built a platform that snaps all these pieces together automatically, and when the computers get stuck, it asks real people to help sort things out through a crowdsourcing app. The result is a single, clear picture of what's really happening on city streets, so transport companies and municipalities can make smarter decisions.

By the numbers

consortium partners across the project

countries represented in the consortium

64%

industry partner ratio in the consortium

data value chains built (urban mobility and public transport)

demo deliverables with working prototypes

total project deliverables produced

industry partners involved in development

The business problem

What needed solving

Cities generate massive amounts of transport data — from bus GPS trackers, road sensors, weather stations, traffic cameras, and news feeds — but this data sits in isolated systems that don't communicate. Companies and municipalities trying to build smart mobility services waste enormous time and engineering effort manually combining incompatible data formats, languages, and update speeds. Without integration, transport decisions are made on incomplete information, leading to inefficient routing, poor service planning, and missed opportunities.

The solution

What was built

The project delivered a final integrated QROWD platform for cross-sectoral big data integration, an MVP road information service with traffic pattern detection built for TomTom, spatiotemporal analytics for large-scale distributed data processing, a data acquisition and linked data generation toolset, the iLog crowdsourcing mobile app for citizen data collection, and crowdsourcing middleware — totaling 34 deliverables including 9 working demos.

Audience

Who needs this

Navigation and mapping companies needing to enrich road data from multiple sourcesMunicipal transport authorities integrating real-time and historical mobility dataSmart city platform vendors building urban data infrastructurePublic transport operators combining passenger, weather, and infrastructure dataLogistics companies needing unified city-level traffic intelligence

Business applications

Who can put this to work

Urban Mobility & Navigation

enterprise

Target: Navigation and mapping companies like TomTom

If you are a navigation or mapping company dealing with fragmented road data from dozens of sources in different formats — this project developed an MVP road information service with pattern detection algorithms and spatiotemporal analytics that enrich your existing data catalogue. The prototype was built directly with TomTom and includes classifiers that recognize specific traffic patterns across large-scale datasets.

Public Transport Operations

enterprise

Target: Municipal transit authorities and public transport operators

If you are a public transport operator struggling to combine real-time passenger data, weather conditions, and infrastructure information into one usable view — this project built 2 data value chains specifically for urban mobility and public transportation. The integrated QROWD platform processes both streaming data and stored data simultaneously, giving you a comprehensive city traffic overview.

Smart City Solutions

mid-size

Target: Smart city technology providers and urban data analytics firms

If you are a smart city solutions provider needing to integrate heterogeneous multilingual datasets from sensors, open data portals, and citizen reports — this project created a data acquisition and linked data generation toolset that automatically collects and standardizes data from multiple sources. The crowdsourcing middleware lets you tap citizens for data validation and gap-filling at scale.

Frequently asked

Quick answers

What would it cost to license or adopt this technology?

The project was publicly funded EU research (Innovation Action), so core results are likely available under open or negotiable licensing terms. Specific pricing would need to be discussed directly with the consortium partners. Contact the University of Southampton or the industry partners for licensing details.

Can this scale to a full city or national transport network?

The platform was designed for large-scale distributed spatiotemporal data processing and was tested with real urban data across 2 data value chains covering urban mobility and public transportation. The architecture handles both data-in-motion (real-time streams) and data-at-rest (stored datasets), which is essential for city-scale deployment.

Who owns the intellectual property?

IP from EU Innovation Actions is typically owned by the consortium partners who generated it. With 7 industry partners including TomTom, some components may already be commercially protected. Contact the coordinator at University of Southampton to clarify IP status for specific components.

How does this handle data from different countries and languages?

The project explicitly built cross-lingual technology to handle multilingual datasets, tested across 6 countries (Switzerland, Germany, Spain, Italy, Poland, UK). The linked data generation tools convert heterogeneous sources into standardized formats using W3C standards.

Is this still maintained or is it abandoned research?

The project ended in November 2019, so active development has concluded. However, the 11-partner consortium with 64% industry ratio means commercial spin-offs or continued development by partners like TomTom is possible. Check the project website at qrowd-project.eu for current status of individual tools.

What about data privacy and GDPR compliance?

The project used crowdsourcing through the iLog app which collects mobility data from users. Based on available project data, the platform was developed during the GDPR implementation period (2016-2019) within the EU context. Specific privacy measures would need to be verified with the consortium.

Can we integrate this with our existing systems?

The platform was built on W3C standards and uses linked data (RDF) formats, which are designed for interoperability. The data acquisition tools automatically connect to heterogeneous data sources, and the architecture separates data ingestion, processing, and analytics into distinct components that can be adopted independently.

Consortium

Who built it

The QROWD consortium is heavily industry-driven with 7 out of 11 partners (64%) coming from industry, which is a strong signal that results are commercially oriented. The project spans 6 countries across Europe, giving it geographic diversity for testing across different urban environments and data standards. The coordinator is the University of Southampton (UK), a top-tier research university providing scientific credibility, while industry partners like TomTom brought real-world road data and commercial use cases. With only 1 SME in the mix, this is primarily a large-player consortium, which means the technology was designed for enterprise-scale deployment rather than lightweight tools for small companies.

UNIVERSITY OF SOUTHAMPTONCoordinator · UK
COMUNE DI TRENTOparticipant · IT
INMARK EUROPA SAparticipant · ES
TOMTOM DEVELOPMENT GERMANY GMBHparticipant · DE
TOMTOM LOCATION TECHNOLOGY GERMANY GMBHparticipant · DE
ATOS SPAIN SAparticipant · ES
UNIVERSITA DEGLI STUDI DI TRENTOparticipant · IT
INSTITUT FUR ANGEWANDTE INFORMATIK (INFAI) EVparticipant · DE

How to reach the team

University of Southampton (UK) — reach out through their research partnerships office or check the project website for named contacts

Order a report on this consortium See UNIVERSITY OF SOUTHAMPTON profile →

Next steps

Talk to the team behind this work.

Want to connect with the QROWD team about licensing their urban data integration platform? SciTransfer can arrange an introduction to the right consortium partner for your specific use case.

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