Data-Driven Analysis on Inter-City Commuting Decisions in Germany

Hui Chen, Sven Voigt and Xiaoming Fu
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Hui Chen: School of Chinese Language and Literature, Beijing Foreign Studies University, Beijing 100089, China
Sven Voigt: Institute of Computer Science, University of Göttingen, 37077 Göttingen, Germany
Xiaoming Fu: Institute of Computer Science, University of Göttingen, 37077 Göttingen, Germany

Sustainability, 2021, vol. 13, issue 11, 1-24

Abstract: Understanding commuters’ behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location’s industry sectors are important factors in commuting decisions. In addition, different age, gender and income groups have different commuting patterns. We employed several machine learning algorithms to predict the commuter number using the identified related features with reasonably good accuracy.

Keywords: commuting; employment; housing price; GDP; income; big data; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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