This paper presents a mobility-resource ownership model. The model captures inter-related personal mobility decisions: which transport mode (out of those available to a decision-maker) to use for a particular trip and which mobility resources (e.g., car, bicycle, transit season ticket or a combination) should the decision-maker own to enable the most “appropriate” set of transport modes. Importantly, the mobility decisions are not evaluated only for a single trip or a single day. In fact, for each decision-maker, an entire set of trips, observed over multiple days, is evaluated. We call this personal accessibility to travel. We present a two-step discrete choice model that includes both mode choice and ownership decisions. The model is estimated based on household travel survey data from Germany. This paper also investigates the simulation of travel times for non-chosen modes that are required as an input. The estimation results show significant effects of the personal accessibility and travel frequency on mobility-resource ownership decisions. To further validate the estimation, the forecasting and sensitivity analysis of the model for different scenarios is evaluated. The proposed model offers an efficient solution to situations when the impact of transport sustainability measures on mobility behaviour needs to be plausibly predicted.

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.