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Understanding urban dynamics from massive mobile traffic data

Understanding urban dynamics from massive mobile traffic data
Understanding urban dynamics from massive mobile traffic data
Understanding the patterns of mobile data consumption is extremely valuable to reveal human activities and ecology in urban areas. This task is nontrivial in terms of three challenges: the complexity of mobile data consumption in large urban environment, the disturbance of abnormal events, and lack of prior knowledge for urban traffic patterns. We propose a novel approach to design a
powerful system that consists of three subsystems: time series decomposing of mobile traffic data, extracting patterns from different components of the original traffic, and detecting anomalous events from noises. Our investigation involving the mobile traffic records of 6,400 cellular towers in Shanghai reveals three important observations. First, among all the 6,400 cellular towers, we identify five daily patterns corresponding to different human daily activity patterns. Second, we find that two natural patterns can be extracted from the weekly trend of mobile traffic consumption, which reflects modes of human activities. Last but not least, besides the regular patterns, we investigate how irregular activities affect mobile traffic consumption, and exploit this knowledge to successfully detect unusual events like concerts and soccer matches. Our proposed methodology therefore will aid a comprehensive understanding of large-scale mobile traffic consumption in urban areas.
266-278
Zhang, Mingyang
81f9c6e0-e098-4c30-93c4-dc59e3c5d505
Fu, Haohao
84732772-190b-4776-b3a5-05f111b0ea11
Li, Yong
0817e950-114f-47f3-aefe-74bf9ec0e2a3
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Zhang, Mingyang
81f9c6e0-e098-4c30-93c4-dc59e3c5d505
Fu, Haohao
84732772-190b-4776-b3a5-05f111b0ea11
Li, Yong
0817e950-114f-47f3-aefe-74bf9ec0e2a3
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Zhang, Mingyang, Fu, Haohao, Li, Yong and Chen, Sheng (2019) Understanding urban dynamics from massive mobile traffic data. IEEE Transactions on Big Data, 5 (2), 266-278.

Record type: Article

Abstract

Understanding the patterns of mobile data consumption is extremely valuable to reveal human activities and ecology in urban areas. This task is nontrivial in terms of three challenges: the complexity of mobile data consumption in large urban environment, the disturbance of abnormal events, and lack of prior knowledge for urban traffic patterns. We propose a novel approach to design a
powerful system that consists of three subsystems: time series decomposing of mobile traffic data, extracting patterns from different components of the original traffic, and detecting anomalous events from noises. Our investigation involving the mobile traffic records of 6,400 cellular towers in Shanghai reveals three important observations. First, among all the 6,400 cellular towers, we identify five daily patterns corresponding to different human daily activity patterns. Second, we find that two natural patterns can be extracted from the weekly trend of mobile traffic consumption, which reflects modes of human activities. Last but not least, besides the regular patterns, we investigate how irregular activities affect mobile traffic consumption, and exploit this knowledge to successfully detect unusual events like concerts and soccer matches. Our proposed methodology therefore will aid a comprehensive understanding of large-scale mobile traffic consumption in urban areas.

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Accepted/In Press date: 30 November 2017
Published date: 1 June 2019

Identifiers

Local EPrints ID: 431642
URI: http://eprints.soton.ac.uk/id/eprint/431642
PURE UUID: 73158465-6ff7-4fbb-ad76-5cbf12222bae

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Date deposited: 11 Jun 2019 16:30
Last modified: 06 Oct 2020 19:22

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