Data-Driven Analysis on Inter-City Commuting Decisions in Germany
1
School of Chinese Language and Literature, Beijing Foreign Studies University, Beijing 100089, China
2
Institute of Computer Science, University of Göttingen, 37077 Göttingen, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Tan Yigitcanlar
Sustainability 2021, 13(11), 6320; https://doi.org/10.3390/su13116320
Received: 20 April 2021 / Revised: 23 May 2021 / Accepted: 26 May 2021 / Published: 2 June 2021
(This article belongs to the Special Issue Reviews and Perspectives on Smart and Sustainable Metropolitan and Regional Cities)
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.
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Keywords:
commuting; employment; housing price; GDP; income; big data; prediction
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MDPI and ACS Style
Chen, H.; Voigt, S.; Fu, X. Data-Driven Analysis on Inter-City Commuting Decisions in Germany. Sustainability 2021, 13, 6320. https://doi.org/10.3390/su13116320
AMA Style
Chen H, Voigt S, Fu X. Data-Driven Analysis on Inter-City Commuting Decisions in Germany. Sustainability. 2021; 13(11):6320. https://doi.org/10.3390/su13116320
Chicago/Turabian StyleChen, Hui, Sven Voigt, and Xiaoming Fu. 2021. "Data-Driven Analysis on Inter-City Commuting Decisions in Germany" Sustainability 13, no. 11: 6320. https://doi.org/10.3390/su13116320
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