Characterizing Tourist Daily Trip Chains Using Mobile Phone Big Data

TL;DR

This study introduces a model to analyze tourist daily trip chains using mobile phone big data, revealing key travel patterns, transfer behaviors, and the influence of the principle of least effort on tourist trip structures.

Contribution

It presents a novel quantitative model for characterizing tourist trip chains and uncovers underlying travel patterns from large-scale mobile data, filling a gap in tourism research.

Findings

Most trip patterns can be captured by 13 key chains.

High probability of transfer between original and common chains over two days.

Tourists' trip structures are influenced by the principle of least effort.

Abstract

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.