1. Mobile Ghent Mobile positioning data and transport: a theoretical, methodological and empirical discussion 24 October 2013 Bert van Wee Delft University of Technology The Netherlands

2. Presentation: focus on travel behaviour Theoretical options follow (mainly) from data. Therefore: data first Not addressed, but very relevant: Privacy restricted versus not. Privacy, availability, legal aspects: probably dynamic. Role of government very important Open systems: more difficult to manage

3. Methods / data (partly linked to theory) Way more data – larger numbers, statistical significance Cheaper Better quality (though not always) External quality checks Use of ‘wisdom of the crowds’ Easier to collect More options for (consistent) longitudinal data collection

4. Methods / data (continued) Solution of underreporting short trips Solution for respondents getting tired of repeatedly reporting Rare events / difficult to select target groups. Start selecting people at destination (as opposed to panel / selection via questions)

5. Methods (partly linked to theory) Combine ‘origin based’ (persons) with destination based (activity/destination) Why would people participate? Rewards. (Airmiles)

6. Theory: Why impacts theory: More data (numbers, data per person) (non)response, disaggregation, impact behaviour Not really fundamentally different. Nevertheless:

7. Theory: Options to test new theoretical assumptions e.g. due to larger numbers, more data per person Options to discover new insights or formulate hypotheses not based on a priori theory (Grounded Theory, data mining). A bit risky, but also new challenges Options to disaggregate further (e.g. mobility trends for specific groups of people)

8. Theory: More locational detail: enrich related theories. More longitudinal data: causalities. Examples: Testing theory of constant Travel Time Budgets: multiple days, also short trips. Desaggregations. Route choice under multiple conditions (e.g. weather) Mode choice in case of changing mode choice (1 person) Shopping behaviour (incl. fun shopping)

9. However Practice so far: The more (bigger) data, the less theoretical underpinnings, the less quality of analyses Data mining Maybe lack of awareness quality data Ignorance of self-selection effects (e.g. leave smartphone at home for short trips; PT: smart phone users versus others) Privacy (may even be linked to self-selection)

10. Empirical Adaptive and flexible event management What do people do in case of emergency? Otherwise very difficult to measure Time space geography: action spaces: more and better data Traffic flow (road, cars): many data, dynamics over time, input for Satnav, short term forecasting: 1.changes in speeds, flows 2.if people would announce destination

11. Walking, cycling (now often poor data) Travel and activities during holiday Better links between travel and activities: not only ‘shopping’ but what kind of shopping (working, recreation)

13. Recreational travel behaviour (some studies ignore recreational travel) Discover ‘bottlenecks’ / validate complaints of citizens ‘Objective’ data for prioritization of plans

14. Maybe police: Speeding Drivers of lorries: too long hours? However: Legal aspects Privacy (big brother is watching you)

15. Other remarks We need to learn. Risk of publication bias: only successful projects reported. Network important! This topic: one of many on Big (and partly open) Data. Learn from lessons outside transport! Lot of literature in other areas (ICT), lot of grey literature

16. Primary reflection: substitution for other data collection methods. Practice: generation (new ideas, new options). Future will show I overlooked key impacts on theory, data, empirical options. Reasons why we have mobile position based data has an impact on behaviour. E.g. train instead of car because of being online.