Spatio-Temporal Analysis and Prediction of Cellular Traffic in Metropolis

Wang, Xu; Zhou, Zimu; Xiao, Fu; Xing, Kai; Yang, Zheng; Liu, Yunhao; Peng, Chunyi

doi:10.1109/tmc.2018.2870135

Cited by 135 publications

(87 citation statements)

References 41 publications

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“…These data can be effectively modelled by dedicated deep learning architectures, such as PointNet++ [100] and Graph CNN [101]. Employing these architectures has great potential to revolutionize the geometric mobile data analysis [102].…”

Section: )mentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,328

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Section: )mentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,328

838

View full text Add to dashboard Cite

show abstract

“…Ferrari et al [12] partition the urban area into grids and agglomerate cellular usage data in each grid to detect events in city. Wang et al [30] study at cellular tower level to predict future traffic in the city.…”

Section: Urban Sensing With Cellular Networkmentioning

confidence: 99%

Urban Scale Trade Area Characterization for Commercial Districts with Cellular Footprints

Zhao

Zhou

Wang

et al. 2020

ACM Trans. Sen. Netw.

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Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data and social network check-in data, MFRs can model customer mobility patterns comprehensively at urban scale. CellTradeMap extracts robust location information from the irregularly sampled, noisy MFRs, adapts the generic trade area analysis framework to incorporate cellular data, and enhances the original trade area model with cellular-based features. We evaluate CellTradeMap on two large-scale cellular network datasets covering 3.5 million and 1.8 million mobile phone users in two metropolis in China, respectively. Experimental results show that the trade areas extracted by CellTradeMap are aligned with domain knowledge and CellTradeMap can model trade areas with a high predictive accuracy.

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“…A number of studies has attempted to analyze urban mobility from a temporal perspective. [43] use centrality measures for temporal prediction on OD networks built from cellular traffic data. [24], study temporal OD networks with change detection techniques for identifying "change points" in time, in which the entire structure of the graph changes.…”

Section: Plos Onementioning

confidence: 99%

Ranking places in attributed temporal urban mobility networks

et al. 2020

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Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socioeconomic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socioeconomic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socioeconomic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.

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Spatio-Temporal Analysis and Prediction of Cellular Traffic in Metropolis

Cited by 135 publications

References 41 publications

Deep Learning in Mobile and Wireless Networking: A Survey

Deep Learning in Mobile and Wireless Networking: A Survey

Urban Scale Trade Area Characterization for Commercial Districts with Cellular Footprints

Ranking places in attributed temporal urban mobility networks

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