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

Combining energy consumption and energy generation by PV systems Maria Kugler

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

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

Data collection | 20 Families Maria Kugler

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

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

Development of a mobility schedule Maria Kugler

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

Impact of repetitive mobility Maria Kugler

Mobility variability based on data Maria Kugler

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

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

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

Reference weeks mileage Maria Kugler

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

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