Analysis of travel activity determinants using robust statistics

Václav Plevka (), Pieter Segaert (), Chris M. J. Tampère () and Mia Hubert ()
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Václav Plevka: KU Leuven
Pieter Segaert: KU Leuven
Chris M. J. Tampère: KU Leuven
Mia Hubert: KU Leuven

Transportation, 2016, vol. 43, issue 6, No 3, 979-996

Abstract: Abstract This study investigates travel behavior determinants based on a multiday travel survey conducted in the region of Ghent, Belgium. Due to the limited data reliability of the data sample and the influence of outliers exerted on classical principal component analysis, robust principal component analysis (ROBPCA) is employed in order to reveal the explanatory variables responsible for most of the variability. Interpretation of the results is eased by utilizing ROSPCA. The application of ROSPCA reveals six distinct principal components where each is determined by a few variables. Among others, our results suggest a key role of variable categories such as journey purpose-related impedance and journey inherent constraints. Surprisingly, the variables associated with journey timing turn out to be less important. Finally, our findings reveal the critical role of outliers in travel behavior analysis. This suggests that a systematic understanding of how outliers contribute to observed mobility behavior patterns, as derived from travel surveys, is needed. In this regard, the proposed methods serve for processing raw data typically used in activity-based modelling.

Keywords: Travel behavior; Determinants; Outliers; Robust sparse principal component analysis (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11116-016-9718-2

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