Ursula Pfefferkorn, German Aerospace Center (DLR) > Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 Approaches to Create a Data Basis for Modelling Long-Distance Travel Behaviour Ursula Pfefferkorn, German Aerospace Center (DLR)

Motivation Long-distance travel („LDT“) is… … relevant … complex > Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 Motivation Long-distance travel („LDT“) is… … relevant … complex … unknown

Motivation An explicit quantification of LDT in status-quo > Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 Motivation What is missing? An explicit quantification of LDT in status-quo A comprehensive micro-level data basis on LDT A socio-demographically differentiated multimodal LDT model

Content of this presentation > Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 Content of this presentation What is long-distance travel? What data is available? What are approaches to capture long-distance travel? What is the „true“ annual trip frequency? How does the idea for the data fusion look like?

What is LDT? What data is available? > Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 What is LDT? What data is available? Different coverage of LDT segments: All segments in one data set (e. g. diary surveys, special long-distance surveys) Only certain segments: Certain purposes („business“, „holiday“, etc.) Duration (2+ days, 5+ days) Motivation (touristic travel and everyday travel) Two levels of data availability: Level of individual respondents = micro-level Level of aggregate figures the „common ground“: a certain distance LDT = Trips > 100 km

What is the „true“ annual trip frequency? > Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 What is the „true“ annual trip frequency?

What can be concluded from that? > Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 What can be concluded from that? Single data sets lead to lower trip frequencies than the approach of a combination of data sources The true value for the annual long-distance trip frequency almost certainly lies somewhere between 8 and 17 It is very likely that the value is higher than 12.9, when different data sources are taken into consideration The data basis of a long-distance travel model should consist of the information of different specialized surveys.

Generation of a micro data set > Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 Generation of a micro data set

> Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 Summary & Outlook The wide range for long-distance trip-frequencies reveals today’s huge uncertainties relating to the quantification of long-distance travel demand. Approaches which aim to capture long-distance travel in one survey are assumed to underrepresent long-distance travel demand The challenge will be to create a combined data set that is free of overlapping of the single travel segments to avoid overestimation of long-distance trip frequencies. In forthcoming work the methods of the single parts (data fusion, calibration, consolidation, and evaluation) will be concretised and applied.

Thank you for your kind attention. > Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 Thank you for your kind attention.

> Approaches to create a data basis for modelling long-distance travel behaviour > U. Pfefferkorn > 05.10.2016 References Axhausen, K. W., et al. (2003). Capturing Long-Distance Travel. Baldock, Hertfordshire, England: Research Studies Press Ltd. Frei, A., et al. (2010). Long distance travel in Europe today: experiences with a new survey: ETH, Eidgenössische Technische Hochschule Zürich, IVT, Institut für Verkehrsplanung und Transportsysteme. Frick, R. and B. Grimm. (2014). Langstreckenmobilität – Aktuelle Trends und Zukunftsperspektiven, Grundlagenstudie. Retrieved from Bern / Kiel: Harrer, B. D., et al. (2013). Tagesreisen der Deutschen: dwif e. V. Hautzinger, H., et al. (2005). Erstellung von Microdatenfiles zu Ein-und Mehrtagesreisen auf Basis der Erhebungen MiD und DATELINE, Schlussbericht. Institut für angewandte Verkehrs-und Tourismusforschung eV, Heilbronn/Mannheim. infas and DLR. (2010). Mobilität in Deutschland 2008. Basisdatensatz. Survey on behalf of the German Federal Ministry of Transport, Building and Urban Development. Karlsruher Institut für Technologie (KIT). (2014). Deutsches Mobilitätspanel 2013. Basisdatensatz. Survey on behalf of the German Federal Ministry of Transport and Digital Infrastructure. Schneider, J. (2009). Geschäftsreisende 2009. Strukturen—Einstellungen—Verhalten. Studie der Internationalen Fachhochschule Bad Honnef-Bonn in Kooperation mit dem Marktforschungsinstitut Infas, Bonn. Sonntag, U. and R. Schrader. (2016) Reiseanalyse 2016. Vol. 46. Erste ausgewählte Ergebnisse der Reiseanalyse. Kiel: FUR Forschungsgemeinschaft Urlaub und Reisen e.V. VDR Verband Deutsches Reisemanagement e.V. (2016) VDR Geschäftsreiseanalyse 2016. Vol. 14. Frankfurt a. M. Zumkeller, D., et al. (2005). Die intermodale Vernetzung von Personenverkehrsmitteln unter Berücksichtigung der Nutzerbedürfnisse (INVERMO). Schlussbericht, März.