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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 urbanmobility patterns and transport mode choices can be derived from cellphone calldetail records coupled with public transport data recorded from smart cards.Specifically, we present new data mining approaches to determine the spatialand temporal variability of public and private transportation usage andtransport mode preferences across Singapore. Our results, which were validatedby Singapore's quadriennial Household Interview Travel Survey (HITS), revealedthat 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 weakor underserved, the analysis shows that the mode share of public transport useincreases from 38 percent in the morning to 44 percent around mid-day and 52percent in the evening.

Digital breadcrumbs: Detecting urban mobility patterns and transport mode choices from cellphone networks