2020•Unpublished venueRequires access

Determination of long-distance travel demand: How to merge distinct data sources into a meaningful and consistent picture?

Angelika Schulz, Tobias Kuhnimhof, Miriam Magdolen, Bastian Chlond, Katja Köhler

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Abstract

Although for most travel segments data are available, there is no up-to-date and consistent overall picture of long-distance travel demand, neither in terms of total transport volumes, nor in terms of socio-demographic characteristics of the corresponding population and the driving forces. Analyses in this respect, however, require a meaningful data framework. The challenge is to gather and prepare available data according to a suitable definition of 'long-distance travel' and to close data gaps based on complementary data collection or any other empirically founded assumptions. The objective of this paper is fourfold: First, the challenge of data merging is described against the background of diverging definitions of long-distance travel. Secondly, the adopted approach of 'data fusion' is presented. Thirdly, the current picture of long-distance travel in Germany is outlined. Finally, methodological issues and challenges of data collection and data merging focusing on long-distance travel are discussed.

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What this paper is about

Although for most travel segments data are available, there is no up-to-date and consistent overall picture of long-distance travel demand, neither in terms of total transport volumes, nor in terms of socio-demographic characteristics of the corresponding population and the driving forces. Analyses in this respect, however, require a meaningful data framework. The challenge is to gather and prepare available data according to a suitable definition of 'long-distance travel' and to close data gaps based on complementary data collection or any other empirically founded assumptions. The objective of this paper is fourfold: First, the challenge of data merging is described against the background of diverging definitions of long-distance travel. Secondly, the adopted approach of 'data fusion' is presented. Thirdly, the current picture of long-distance travel in Germany is outlined. Finally, methodological issues and challenges of data collection and data merging focusing on long-distance travel are discussed.

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Available abstract

Although for most travel segments data are available, there is no up-to-date and consistent overall picture of long-distance travel demand, neither in terms of total transport volumes, nor in terms of socio-demographic characteristics of the corresponding population and the driving forces. Analyses in this respect, however, require a meaningful data framework. The challenge is to gather and prepare available data according to a suitable definition of 'long-distance travel' and to close data gaps based on complementary data collection or any other empirically founded assumptions. The objective of this paper is fourfold: First, the challenge of data merging is described against the background of diverging definitions of long-distance travel. Secondly, the adopted approach of 'data fusion' is presented. Thirdly, the current picture of long-distance travel in Germany is outlined. Finally, methodological issues and challenges of data collection and data merging focusing on long-distance travel are discussed.

Key concepts: Merge (version control), Data collection, Computer science, Travel behavior, Travel time, Data science, Data mining, Econometrics

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Determination of long-distance travel demand: How to merge distinct data sources into a meaningful and consistent picture? — Research Paper | ScholarLens