  Many modern and growing cities are facing declines in public transport usage,
with few efficient methods to explain why. In this article, we show that urban
mobility patterns and transport mode choices can be derived from cellphone call
detail records coupled with public transport data recorded from smart cards.
Specifically, we present new data mining approaches to determine the spatial
and temporal variability of public and private transportation usage and
transport mode preferences across Singapore. Our results, which were validated
by Singapore's quadriennial Household Interview Travel Survey (HITS), revealed
that there are 3.5 (HITS: 3.5 million) million and 4.3 (HITS: 4.4 million)
million inter-district passengers by public and private transport,
respectively. Along with classifying which transportation connections are weak
or underserved, the analysis shows that the mode share of public transport use
increases from 38 percent in the morning to 44 percent around mid-day and 52
percent in the evening.
