Filtering and windowing mobile traffic time series for territorial land use classification

Calegari, Gloria Re; Carlino, Emanuela; Peroni, Diego; Celino, Irene

doi:10.1016/j.comcom.2016.04.016

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…In recent years, many studies have made full use of big data for urban land use classification or UFA detection [9,10]. For example, the number of regional mobile phone calls has been used to represent the characteristics of urban functions [11], and points of interest (POIs) data have been collected to demonstrate the land use of an area [12,13].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

Gang

Chen

Sun

et al. 2020

Journal of Advanced Transportation

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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.

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Section: Introductionmentioning

confidence: 99%

Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

Gang

Chen

Sun

et al. 2020

Journal of Advanced Transportation

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“…We can not only aggregate the data into a week or a day [41,42] but also divide a week to obtain greater detail, such as distinguishing human activity patterns on weekdays and weekends [58] and distinguishing human activity patterns on normal workdays, Fridays, Saturdays and Sundays [51]. In addition, the interval of time series can be set as needed, such as 10 minutes or one hour [59,60]. The study area should be divided into various unclassified areas, but the division method can be flexibly selected, such as dividing based on grids or traffic analysis zones (TAZs).…”

Section: Extracting Features and Constructing Time Seriesmentioning

confidence: 99%

An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification

He²,

Zhang

et al. 2019

IJGI

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Urban land use information is critical to urban planning, but the increasing complexity of urban systems makes the accurate classification of land use extremely challenging. Human activity features extracted from big data have been used for land use classification, and fusing different features can help improve the classification. In this paper, we propose a framework to integrate multiple human activity features for land use classification. Features were fused by constructing a membership matrix reflecting the fuzzy relationship between features and land use types using the fuzzy c-means (FCM) clustering method. The classification results were obtained by the fuzzy comprehensive evaluation (FCE) method, which regards the membership matrix as the fuzzy evaluation matrix. This framework was applied to a case study using taxi trajectory data from Nanjing, and the outflow, inflow, net flow and net flow ratio features were extracted. A series of experiments demonstrated that the proposed framework can effectively fuse different features and increase the accuracy of land use classification. The classification accuracy achieved 0.858 (Kappa = 0.810) when the four features were fused for land use classification.

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RUMP: Resource Usage Multi-Step Prediction in Extreme Edge Computing

Kain

Elsayed

Chen

et al. 2023

Computer Communications

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Filtering and windowing mobile traffic time series for territorial land use classification

Cited by 5 publications

References 11 publications

Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification

RUMP: Resource Usage Multi-Step Prediction in Extreme Edge Computing

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