Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo

Chuan Ding, Jason Cao, Petter Næss

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Although many studies have explored the relationship between the built environment and travel behavior, the literature offers limited evidence about the collective influence of built environment attributes, and their non-linear effects on travel. This study innovatively adopts gradient boosting decision trees to fill the gaps. Using data from Oslo, we apply this method to the data on both weekdays and weekends to illustrate the differential effects of built environment characteristics on driving distance. We found that they have a stronger effect on weekdays than on weekends. On weekdays, their collective influence is larger than that of demographics. Furthermore, they show salient non-linear effects on driving distance in both models, challenging the linearity assumption commonly adopted in the literature. This study also identifies effective ranges of distance to different centers and population density, and highlights the important role of sub-centers in driving reduction.

Original languageEnglish (US)
Pages (from-to)107-117
Number of pages11
JournalTransportation Research Part A: Policy and Practice
Volume110
DOIs
StatePublished - Apr 2018

Keywords

  • Compact development
  • Employment center
  • Land use
  • Machine learning
  • Travel behavior

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