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    Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data

    Source: Journal of Transportation Engineering, Part A: Systems:;2021:;Volume ( 147 ):;issue: 010::page 04021070-1
    Author:
    Haodong Sun
    ,
    Yanyan Chen
    ,
    Jianhui Lai
    ,
    Yang Wang
    ,
    Xiaoming Liu
    DOI: 10.1061/JTEPBS.0000580
    Publisher: ASCE
    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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      Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data

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    contributor authorHaodong Sun
    contributor authorYanyan Chen
    contributor authorJianhui Lai
    contributor authorYang Wang
    contributor authorXiaoming Liu
    date accessioned2022-02-01T21:42:39Z
    date available2022-02-01T21:42:39Z
    date issued10/1/2021
    identifier otherJTEPBS.0000580.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4271887
    description abstractNowadays, 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.
    publisherASCE
    titleIdentifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data
    typeJournal Paper
    journal volume147
    journal issue10
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.0000580
    journal fristpage04021070-1
    journal lastpage04021070-11
    page11
    treeJournal of Transportation Engineering, Part A: Systems:;2021:;Volume ( 147 ):;issue: 010
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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