  Tourists tend to visit multiple destinations out of their variety-seeking
motivations in their trips. Thus, it is critical to discover travel patterns
involving multi-destinations in tourism research. Existing relevant research
most relied on survey data or focused on citizens due to the lack of
large-scale, fine-grained tourism datasets. Several scholars have mentioned the
notion of trip chains, but few works have been done towards quantitatively
identifying the structures of trip chains. In this paper, we propose a model
for quantitatively characterizing tourist daily trip chains. After applying
this model to tourist mobile phone big data, underlying tourist travel patterns
are discovered. Through the framework, we find that: (1) Most "hybrid"
(inter-city and intra-city) and "intra-city" (only intra-city) patterns can be
captured by only 13 key trip chains relatively; (2) For two continuous days,
almost all kinds of original chains have a rather high probability to transfer
to either the first two transferred chains, or other infrequent chains in our
study areas; (3) The principle of least efforts (PLE) affects tourists'
structures of trip chains. We can use average degree and average travel
distance to interpret tourist travel behavior (achieving tasks in PLE). This
study not only demonstrate the complex daily travel trip chains from tourism
big data, but also fill the gap in tourism literature on multi-destination
trips by discovering significant and underlying patterns based on mobile
datasets.
