1. SHANTI project: Main Results and Recommendations k Presentation at Eurostat Workshop on Passenger Mobility, June 17 2013 by Shanti Consortium, Speaker: Tobias Kuhnimhof

2. SHANTI – Survey HArmonisation with New Technologies Improvement  20 European countries + IL + AUS  WG1 Methods and tools  WG2 Use of new technologies  WG3 Vehicle-based surveys  WG4 Household travel surveys

3. Agenda „There‘s a mobility data treasure in Europe….  Overview of EU NTSs …waiting to seized with suitable approaches.  The great potential of ex-post harmonization However, information gaps remain or emerge….  Challenges in the data scape …asking for new surveys, methods, & technologies.“  Recommendations for future activities

4. Overview of EU NTSs „There‘s mobility data treasure in Europe…“

5. In large parts of Europe, recent NTS data is available. Most recent NTS After 2005 2000 – 2005 Before 2000

6. In large parts of Europe, long NTS time series exist. First NTS before 1980 1990-1981 2000-1991 after 2000

7. EU NTS Knowledge Base:  Organisation & Costs  Methodology  Coverage  Content  Accessibility  References  … http://shanti-wiki.inrets.fr/http://shanti-wiki.inrets.fr/ - Documentation of the European mobility data treasure.

8. The great potential of ex-post harmonization. „There‘s mobility data treasure in Europe… …waiting to be seized with suitable approaches.“

9. How far do Europeans travel per day? A four country example, based on NTS reports. 2010MiD 2008MOP 20082008

10. How far do Europeans travel per day? A four country example, based on NTS reports. 2010MiD 2008MOP 20082008 Δ 3%

11. How far do Europeans travel per day? A four country example, comparing apples & oranges. 2010MiD 2008MOP 20082008 persons 15+ persons 0+ persons 10+ persons 0+ persons 6+ …plus other differences of definitions

12. How far do Europeans travel per day? A four country example, same population (ages 13-84). 2010MiD 2008MOP 20082008 Δ 7%

13. How far do Europeans travel per day? A four country example, – still: apples & oranges. 2010MiD 2008MOP 20082008 88% trip makers 90% trip makers 92% trip makers 82% trip makers 78% trip makers

14. How far do Europeans travel per day? A four country example, with trip makers as denominator. 2010MiD 2008MOP 20082008 Δ 4%

15. Ex-post harmonized mobility data as a basis for cross-country comparisons: Travel distances.

16. Ex-post harmonized mobility data as a basis for cross-country comparisons: Mode Shares.

17. Options for collection & dissemination of mobility data: Collected data Flexibility to recombine indicators of interest Predefined statistical indicators - Medium resolution cross-tabs + High resolution cross-tabs ++

18. Collected data Flexibility to recombine indicators of interest Predefined statistical indicators - Medium resolution cross-tabs + High resolution cross-tabs ++ Options for collection & dissemination of mobility data: Mobility indicators (e.g. trips, km) broken down in cross-tabs with multiple dimensions (e.g. mode, age, gender, place) plus necessary weights for recombination

19. Collected data Flexibility to recombine indicators of interest Predefined statistical indicators - Medium resolution cross-tabs + High resolution cross-tabs ++ Options for collection & dissemination of mobility data: Today

20. Collected data Flexibility to recombine indicators of interest Predefined statistical indicators - Medium resolution cross-tabs + High resolution cross-tabs ++ Options for collection & dissemination of mobility data: Shanti

21. Options for collection & dissemination of mobility data: Vision for tomorrow‘s data archive? Collected data Flexibility to recombine indicators of interest Predefined statistical indicators - Medium resolution cross-tabs + High resolution cross-tabs ++

22. Challenges in the data scape. „However, information gaps remain or emerge…“

23. DG MOVE‘s needs vs. existing and emerging challenges in the mobility data scape:  Monitor progress on white paper transport objectives  Urban mobility  Long distance (300-1000km) mobility Various countries not covered Financing of surveys Remaining comparability issues Emerging biases Limited comparability of place info Very little data on long distance travel …

24. Recommendations for future activities „However, information gaps remain or emerge… …asking for new surveys, methods & technologies.“

25. For relevant survey properties, various options have emerged. Survey mode:…PAPI…Mode Mix…CAWI…Only GPS… Trip geo info:…Self reported…Combination…Addresses…Geo codes… Type of travel:…All…Simplify some…Omit some… Mode / purpose:… High granularity …Common denominator categories…Too simple… Seasonality:…All year…Representative season… Selective season … Frequency:…Continuous…Annual…Less often…Once… Reporting period…One day…Multiple days…One week…Long periods…

26. Based on existing research & experience Shanti recommends best practice. Survey mode:…PAPI…Mode Mix…CAWI…Only GPS… Trip geo info:…Self reported…Combination…Addresses…Geo codes… Type of travel:…All…Simplify some…Omit some… Mode / purpose:… High granularity …Common denominator categories…Too simple… Seasonality:…All year…Representative season… Selective season … Frequency:…Continuous…Annual…Less often…Once… Reporting period…One day…Multiple days…One week…Long periods…

27. However, there’s big diversity across Europe as regards survey methods. PAPI F2F (GPS) CATI CAWI F2F CAWI Example: Co-existing survey modes F2F CATI (GPS) CATI

28. This diversity indicates that one size may not fit all because … …different approaches are suitable and necessary. …situations of stakeholders differ (data requirements, financing, organisation etc.). …conditions for field work differ (sampling options, legal framework etc.). …respondents differ (language, culture etc.).

29. Acknowledging the need for flexibility, Shanti recommends a range of options. Survey mode:…PAPI…Mode Mix…CAWI…Only GPS… Trip geo info:…Self reported…Combination…Addresses…Geo codes… Type of travel:…All…Simplify some…Omit some… Mode / purpose:… High granularity …Common denominator categories…Too simple… Seasonality:…All year…Representative season… Selective season … Frequency:…Continuous…Annual…Less often…Once… Reporting period…One day…Multiple days…One week…Long periods…

30. Acknowledging the need for flexibility, Shanti recommends a range of options. Survey mode:…PAPI…Mode Mix…CAWI…Only GPS… Trip geo info:…Self reported…Combination…Addresses…Geo codes… Type of travel:…All…Simplify some…Omit some… Mode / purpose:… High granularity …Common denominator categories…Too simple… Seasonality:…All year…Representative season… Selective season … Frequency:…Continuous…Annual…Less often…Once… Reporting period…One day…Multiple days…One week…Long periods…

31. Sensibly combining approaches within the range allows for compatibility with other NTSs. Survey mode:…PAPI…Mode Mix…CAWI…Only GPS… Trip geo info:…Self reported…Combination…Addresses…Geo codes… Type of travel:…All…Simplify some…Omit some… Mode / purpose:… High granularity …Common denominator categories…Too simple… Seasonality:…All year…Representative season… Selective season … Frequency:…Continuous…Annual…Less often…Once… Reporting period…One day…Multiple days…One week…Long periods…

32. But leaving this range likely renders a survey incompatible with other NTSs. Survey mode:…PAPI…Mode Mix…CAWI…Only GPS… Trip geo info:…Self reported…Combination…Addresses…Geo codes… Type of travel:…All…Simplify some…Omit some… Mode / purpose:… High granularity …Common denominator categories…Too simple… Seasonality:…All year…Representative season… Selective season … Frequency:…Continuous…Annual…Less often…Once… Reporting period…One day…Multiple days…One week…Long periods… 

33. Which role can tracking (GPS, Galileo, GSM) and Big Data play for large travel surveys? Shanti recommends a smooth & monitored transition to tracking, possibly with different survey modes in parallel for a while. Administered, traditional travel surveys remain important for now & may even be more useful in the future to link Big Data. Opportunities:  more efficient & possibly less costly  comprehensive, less biased trip coverage  new, unknown types of data with new options Risks:  new unknown biases  discontinuity of times series (past & future)  new incomparabilities across countries  data privacy challenges

34. Conclusions & recommendations for next steps „There‘s a mobility data treasure in Europe waiting to seized with suitable approaches. However, information gaps remain or emerge asking for new surveys, methods, & technologies.“  Make use of existing NTS data & make it usable to others (e.g. in a data archive).  Support research on ex-post harmonization.  Encourage more harmonization of existing NTSs.  Encourage new NTSs with compatible methods.  Encourage smooth introduction of new technologies where sensible.  Close survey data gap on long distance travel.