详情

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

题名：	Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data
正文语种：	eng
作者：	Sun, Haodong;Chen, Yanyan;Lai, Jianhui;Wang, Yang;Liu, Xiaoming
作者单位：	Beijing Univ Technol Beijing Key Lab Traff Engn Beijing 100124 Peoples R China;Beijing Univ Technol Beijing Key Lab Traff Engn Beijing 100124 Peoples R China;Beijing Univ Technol Beijing Key Lab Traff Engn Beijing 100124 Peoples R China;Beijing Univ Technol Beijing Key Lab Traff Engn Beijing 100124 Peoples R China;Beijing Univ Technol Beijing Key Lab Traff Engn Beijing 100124 Peoples R China
关键词：	Tourists behavior;Human mobility;K-means clustering method;Mobile phone signaling data
摘要：	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.
出版年：	2021
期刊名称：	Journal of Transportation Engineering
卷：	147
期：	10