Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 10
Abstract
Nowadays, a large percentage of people use smartphones frequently. The mobile phone signaling data contains various attributes that can be used to infer when and where the user is. Compared with other big data sources (e.g., social media and GPS data) for the human movement, mobile phone signaling data demonstrate the advantages of a high coverage of population, strong temporal continuity, and low cost of collection. Taking advantage of such mobile phone signaling data, this work aims to identify tourists and locals from a large volume of mobile phone signaling data in a tourism city and analyze their spatiotemporal patterns to better promote tourism service and alleviate possible disturbance to local residents. In this paper, we present a framework to differentiate these two types of people by the following procedure: first, the hidden behavior characteristics of users are extracted from mobile phone signaling data; and then, the K-means clustering method is adopted to identify tourists and locals. With the identification of both tourists and local residents, we analyze the distribution and interaction characteristics of tourists and locals in an urban area. An experimental study is conducted in a famous tourism city, Xiamen, China. The results indicate that the proposed method can successfully identify the most popular scenic spots and major transportation corridors for tourists. The feature extraction, identification, and spatiotemporal analysis presented in this paper are of great significance for analyzing the urban tourism demand, managing the urban space, and mining the tourist behavior.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
1.
Mobile phone signaling data of Xiamen: This data are in cooperation with a Xiamen communication operator, and our permission is only allowed to deploy the algorithm on their data platform and calculate the results. Meanwhile, the data cannot be taken out. Therefore, these data are provided with restrictions.
2.
The feature extraction algorithm: The related codes are available from the corresponding author if requested.
3.
The K-means clustering method: This code is also available from the corresponding author if requested.
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 10 • October 2021
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© 2021 American Society of Civil Engineers.
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Received: Oct 28, 2020
Accepted: May 17, 2021
Published online: Aug 8, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 8, 2022
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