Analysis of travel activity determinants using robust statistics

TL;DR

This paper uses robust statistical methods to analyze travel behavior determinants from survey data, highlighting the importance of outliers and identifying key variables influencing travel patterns.

Contribution

It introduces the application of robust PCA techniques (ROBPCA and ROSPCA) to better interpret travel survey data affected by outliers, enhancing activity-based modeling.

Findings

Six principal components identified, each driven by a few variables.

Journey purpose-related factors are most influential.

Journey timing variables are less significant.

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.