1. Development of a Mobility Demand Model for Private Usage under Non-Urban Conditions
Maria Kugler 20th European Conference on Mobility Management – ECOMM 2016 Athen, 02. Juni 2016

2. Combining energy consumption and energy generation by PV systems
Maria Kugler

3. Understanding mobility
Everyday life is marked by routines
“we find a 93% potential predictability […] in user mobility“
[Bia13]
[Son10]
Location routines based on cell-tower connections
Home Work Other
[Far]
Maria Kugler

4. Mobility demand model Criteria Implementation Identity of family
Individual consideration of families
Identity of trip
Identifiers of trips
Home base – Home base
Connected trips
Everyday mobility
Most frequent trips preferred
Time horizon
Reference week; hour-based sectioning
Maria Kugler

5. Data collection | 20 Families
Maria Kugler

6. Data analysis | Raw data
Tracked and preprocessed data analysis Routines over weeks detectable Missing data points due to tracking errors No significant distinction in distance demand for families of same or different type
Weeks Mo Tu We Th Su Fr Sa
Time of Day
Maria Kugler

7. Mobility pattern
Tracked data does not represent the actual long-term mobility demand.
Data statistics are basis for logical mobility model.
Complete weeks
Incomplete weeks
Maria Kugler

8. Development of a mobility schedule
Maria Kugler

9. Time-dependent repetitive mobility
Mobility demand can be clustered in different periods of time.
Maria Kugler

10. Impact of repetitive mobility
Maria Kugler

11. Mobility variability based on data
Maria Kugler

12. User influence and acceptance
Quantitative survey via internet
Survey sample size: n = 80; 55 completed questionnaires
Questions concerning:
Social background
Mobility characteristics
Attitude towards emission potentials
Maria Kugler

13. User acceptance of mobility variability (n=61)
Maria Kugler

14. Reference week
Everyday mobility schedule based on routine trips together with time and logic based frequency.
Maria Kugler

15. Reference weeks mileage
Maria Kugler

16. Mobility Demand Model for Private Usage
General family-dependent weekly mobility demand Quantifiable understanding of everyday non-urban mobility Focus on the trip number Specified by comparable objective analyses (logic, trip purpose, trip variability) Independent factor for understanding mobility
Maria Kugler

17. Development of a Mobility Demand Model for Private Usage under Non-Urban Conditions
Maria Kugler
Thank you for your attention
References
[Bia13] BIAGIONI, J. und KRUMM, J.: Days of Our Lives: Assessing Day Similarity from Location Traces. In: Lecture Notes in Computer Science S. 89–101, 2013.
[Far11] FARRAHI, K. und GATICA-PEREZ, D.: Discovering routines from large-scale human locations using probabilistic topic models. In: ACM Trans. Intell. Syst. Technol. 1, S. 1–27, 2011.
[Son10] SONG, C.; QU, Z.; BLUMM, N. und BARABÁSI, A.-L.: Limits of Predictability in Human Mobility. In: Science 5968, S. 1018–1021, 2010.
Maria Kugler