Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data
With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8956910 |
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| _version_ | 1832566096610197504 |
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| author | Guang Yuan Yanyan Chen Lishan Sun Jianhui Lai Tongfei Li Zhuo Liu |
| author_facet | Guang Yuan Yanyan Chen Lishan Sun Jianhui Lai Tongfei Li Zhuo Liu |
| author_sort | Guang Yuan |
| collection | DOAJ |
| description | With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method. |
| format | Article |
| id | doaj-art-c617b2f7394347818b8a627f760976ec |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c617b2f7394347818b8a627f760976ec2025-02-03T01:05:16ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/89569108956910Recognition of Functional Areas Based on Call Detail Records and Point of Interest DataGuang Yuan0Yanyan Chen1Lishan Sun2Jianhui Lai3Tongfei Li4Zhuo Liu5Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaWith the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.http://dx.doi.org/10.1155/2020/8956910 |
| spellingShingle | Guang Yuan Yanyan Chen Lishan Sun Jianhui Lai Tongfei Li Zhuo Liu Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data Journal of Advanced Transportation |
| title | Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data |
| title_full | Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data |
| title_fullStr | Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data |
| title_full_unstemmed | Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data |
| title_short | Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data |
| title_sort | recognition of functional areas based on call detail records and point of interest data |
| url | http://dx.doi.org/10.1155/2020/8956910 |
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