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Tourists tend to visit multiple destinations out of their variety-seekingmotivations in their trips. Thus, it is critical to discover travel patternsinvolving multi-destinations in tourism research. Existing relevant researchmost relied on survey data or focused on citizens due to the lack oflarge-scale, fine-grained tourism datasets. Several scholars have mentioned thenotion of trip chains, but few works have been done towards quantitativelyidentifying the structures of trip chains. In this paper, we propose a modelfor quantitatively characterizing tourist daily trip chains. After applyingthis model to tourist mobile phone big data, underlying tourist travel patternsare discovered. Through the framework, we find that: (1) Most "hybrid"(inter-city and intra-city) and "intra-city" (only intra-city) patterns can becaptured by only 13 key trip chains relatively; (2) For two continuous days,almost all kinds of original chains have a rather high probability to transferto either the first two transferred chains, or other infrequent chains in ourstudy areas; (3) The principle of least efforts (PLE) affects tourists'structures of trip chains. We can use average degree and average traveldistance to interpret tourist travel behavior (achieving tasks in PLE). Thisstudy not only demonstrate the complex daily travel trip chains from tourismbig data, but also fill the gap in tourism literature on multi-destinationtrips by discovering significant and underlying patterns based on mobiledatasets.

Characterizing Tourist Daily Trip Chains Using Mobile Phone Big Data