Lifting the barriers to data
sharing
Big Data and Transport Models
Roundtable 14-16 December 2020

2Background• “Big and open data” offer new opportunities for transport policymaking• Potential shift in regulatory function of governments– Data-driven policy making > monitoring and enforcement oflegislation• Private sector collect much of the “big data”– Access to these could improve planning and regulation while reducing
survey costs• Need for partnership between public and private sector

3Working Group on Big Data and Open Data• USA (Chair: Patricia Hu, US DOT), Canada, UK, France, Austria,Netherlands, Finland, Italy, Greece, UNECE, SEETO• Assist member countries in leveraging and curating big data by:– Summarising quality standards that could be used to communicate thequality and statistical validity of big and open data
– Developing a framework to facilitate data sharing between the private
and the public sectors

4OECD Data Quality Standards and Big Data
Standard

Big Data Benefits

Big Data Challenges

Relevance

Allows the study of new subjects

Can lack information needed to respond to objectives(socio-demographic, metadata…)

AccuracyMore detail, no human error

Evaluation of validity of results have to be modifiedCredibility---Can lack methods of scientific rigor and objectivity,
although work is being done to improve thisTimeliness

More data at faster rates

Data becomes out-of-date faster, requiring new
methods of analysis

Accessibility

Interpretability

Coherence

Metadata and collection methods can sometimes beCollected quickly / instantaneously
confidential and costly to obtain, assuming proper
documentation has been recorded.
If accessible, level of detail allow for Can lack info on sample, variables of interest or thosemore precise understanding ofneeded for extrapolation of findings. Less control overpopulation
what is collected
Potential to compare between
countries (Google search etc)

Lacks consistent collection and documentation
procedures

5

Case studies of data sharing• Canada: Rail and trucking waybill, Fuel consumption survey• Finland: NordicWay project, Aurora project• Italy: PLUG-IN project• France: E-Logistics database, Use of passive data sources to estimate traffic on regional
trains, , SNCF route planner, Use of GSM data for th estimation of tourism traffic,• Netherlands: KiM Mobility Panel• Greece: Delivering driving analytics to insurance companies

6

Stakeholder interviews• A structured interview– Experience with existing partnership (type, structure, benefits)– Attitudes towards partnership– Third party set-up– Value proposition– Data sets (ownership, data sharing approach, level of detail, conditions, re-sharing,dissemination, open data)• Austria: ASFINAG• Canada: Desjardins Insurance• Greece: Attic Tollway• Germany: PTV• Norway: Scania, PostNord Logistics

7Observations• New models of public-private data sharing partnerships considered
indispensable• There are several types of partnerships (large-scale data-dumps as part ofsurveys, fixed term projects, commercial schemes)…
• with different settings (fixed research projects, business development activities,
mandatory data sharing schemes)…
• and mostly temporary (limited duration, pilot projects, data access only for aperiod of time)• Data typically only shared between parties involved due to data protection andprivacy issues

8Recommendations to facilitate data sharing• Governments should try to make any data received more widely open

• Ensure compliance with privacy protection regulation– Before sharing - anonymisation, encryption of data
– Location, trajectory data most vulnerable – apply the most robust protection methods• Building trust important– Develop and endorse non-disclosure agreements, involve trusted “third party”, develop“safe answers approach” (only query results exchanged instead of raw data)• Apply appropriate partnership model depending on type of data use– Offering to produce timely open data in exchange (can help business)– Financial compensation (recovers investment costs and addresses free-riding)– Mandating (limit to data of public interest, apply purpose specificity and dataminimisation principles)Thank you
Questions & comments to:
Jari Kauppila

E jari.kauppila@itf-oecd.org