Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea. (March 2022)

• Record Type: Journal Article
• Authors: Xu, Yang; Zou, Dan; Park, Sangwon; Li, Qiuping; Zhou, Suhong; Li, Xinyu
• Is Part Of: Computers, environment and urban systems. Volume 92(2022)
• Publication Date: 2022-03
• Subjects: Location prediction -- Mobile phone data -- Deep learning -- Human mobility -- Tourist mobility -- Smart tourism; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2021.101753
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract : Highlights: A quantitative analysis of the movement predictability of urban tourists and visitors using a nationwide mobile phone dataset. A Markov chain model and a recurrent neural network are used to assess travelers' movement predictability. Travelers' movement predictability is impacted by length of stay and activeness in travel patterns. A notable geographic variation is observed: travelers' movements are more predictable in some cities, but less in others. Abstract: The abilities to predict tourist movements are critical to many urban applications, such as travel recommendations, targeted advertising, and infrastructure planning. Despite its importance, our understanding on the movement predictability of urban tourists and visitors is still limited, partially due to difficulties in accessing large scale mobility observations. In this study, we aim to bridge this gap by analyzing a nationwide mobile phone dataset. The dataset captures movement traces of a large number of international travelers who visited South Korea in 2018. By introducing two prediction models, one being Markov chain and the other with a recurrent neural network architecture, we assess how well travelers' movements can be predicted under different model settings, and examine how predictability relates to travelers' length of stay and activeness in travel patterns. Since travelers' destination choices are quite diverse in South Korea, this enables us to further investigate the geographic variation of the models' performance. Results show that the Markov chain model achieves an overall accuracy between 33.4% (@Acc1 metric) and 64.2% (@Acc5 metric), compared to 41.9% (@Acc1) and 67.7% (@Acc5) for the recurrent neural network model. The prediction capabilities of both models are largely unequal across individuals, with active travelers being more predictable in general. There is a notable geographic variation in the models' performance, meaning that travelers' movements are more predictable in some cities, but less in others. We believe this study represents a new effort in portraying the movement predictability of urban tourists and visitors. The analytical framework can be applied to assist tourism planning and service deployment in cities.