  Recent mobility scaling research, using new data sources, often relies on
aggregated data alone. Hence, these studies face difficulties characterizing
the influence of factors such as transportation mode on mobility patterns. This
paper attempts to complement this research by looking at a category-rich
mobility data set. In order to shed light on the impact of categories, as a
case study, we use conventionally collected German mobility data. In contrast
to `check-in'-based data, our results are not biased by Euclidean distance
approximations. In our analysis, we show that aggregation can hide crucial
differences between trip length distributions, when subdivided by categories.
For example, we see that on an urban scale (0 to ~15 km), walking, versus
driving, exhibits a highly different scaling exponent, thus universality class.
Moreover, mode share and trip length are responsive to day-of-week and
time-of-day. For example, in Germany, although driving is relatively less
frequent on Sundays than on Wednesdays, trips seem to be longer. In addition,
our work may shed new light on the debate between distance-based and
intervening-opportunity mechanisms affecting mobility patterns, since mode may
be chosen both according to trip length and urban form.
