The temporal network of mobile phone users in Changchun Municipality, Northeast China

Du, Zhanwei; Yang, Yintang; Gao, Chao; Huang, Liping; Huang, Qiuyang; Bai, Yuan

doi:10.1038/sdata.2018.228

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…With the popularity of data-driven methods, data has become the foundation for urban transportation research today. Although there are some datasets 1 – 3 that represent human mobility have been opened, they have limited benefit for solving transportation issues since these data are collected by non-transportation activities and cannot be interpreted as trip behaviour directly 4 . Hence, the data directly obtained from the transportation system is necessary for transportation researches.…”

Section: Background and Summarymentioning

confidence: 99%

City-scale synthetic individual-level vehicle trip data

Chen

Wang

et al. 2023

Sci Data

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Trip data that records each vehicle’s trip activity on the road network describes the operation of urban traffic from the individual perspective, and it is extremely valuable for transportation research. However, restricted by data privacy, the trip data of individual-level cannot be opened for all researchers, while the need for it is very urgent. In this paper, we produce a city-scale synthetic individual-level vehicle trip dataset by generating for each individual based on the historical trip data, where the availability and trip data privacy protection are balanced. Privacy protection inevitably affects the availability of data. Therefore, we have conducted numerous experiments to demonstrate the performance and reliability of the synthetic data in different dimensions and at different granularities to help users properly judge the tasks it can perform. The result shows that the synthetic data is consistent with the real data (i.e., historical data) on the aggregated level and reasonable from the individual perspective.

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Section: Background and Summarymentioning

confidence: 99%

City-scale synthetic individual-level vehicle trip data

Chen

Wang

et al. 2023

Sci Data

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“…With the prevalence of mobile phone adoption, our digital footprints are silently collected. This mobile data provides an understanding of human mobility [4]. The challenges of smart cities and the need for trade-offs between the use of data and privacy are discussed [2] There are several research papers on the adoption of mobile data to understand urban mobility in urban landscapes [8,14,18].…”

Section: Introductionmentioning

confidence: 99%

Use of mobile digital footprint to understand how COVID-19 impacts the visitors to art places in Singapore

Zeng,

Tuncer

2023

SCS

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As Singapore strengthens its position as a Creative City, the importance of art places is increasingly evident. The time and frequency of visitors spent on the art places are indications of how successful the art places are. The understanding of the footfall for art places using traditional methods, such as ticket sales, and person-count can be expensive and inaccurate. With the prevalence of mobile phone adoption, our digital footprints are silently collected. These digital footprints can be used to understand the number of visitors who have visited art places, as well as their time spent at the different art places. This data is collected by CITYDATA.ai, a geospatial AI platform and registered data broker compliant with global and regional privacy regulations. An interesting but almost unobserved trend is to understand how COVID-19 affects the visitors to art places. During this period, there could also be a fundamental shift in how people feel and react towards art places. The contribution of this paper helps planners to understand the dynamics of change for the visitors to art places and subsequently enables the planners to have the right strategies to reposition Singapore as a Creative City.

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“…Actually, the dynamic property is the necessary feature of complex networks [12]. For example, the personal stream will influence the community structure in the traffic network [13], [14], and a social network will vary with the flexible relationships among users [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Community Detection Based on a Label-Based Swarm Intelligence

Wang

Deng

et al. 2019

IEEE Access

Self Cite

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The dynamic network tails after the development of the real-world that is essential for particle applications such as traffic flow analyses and social network analyses. The requirement of maximizing the quality of the community structure at current time step and minimizing the difference of the community structure between two successive time steps synchronously brings serious challenges to the dynamic community detection. Some existing approaches (i.e., the multi-objective particle swarm optimization, named as DYNMOPSO) utilize the swarm intelligence pattern to solve such a community detection problem in dynamic networks. Nevertheless, the DYNMOPSO has the deficiency of the undesirable prematurity constringency and monotonicity of particles because of the high choice stress. Thus, a label-based swarm intelligence on the basis of the evolutionary clustering framework is presented for overcoming those disadvantages. The label propagation approach initializes the labels of particles and is used for escaping the prematurity constringency. The crossover and mutation methods are introduced to improve the variety of particles and retain preferable the community structure synchronously. Experiments in synthetical and real networks prove that our algorithm is valid and exceeds state-of-the-art approaches.

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The temporal network of mobile phone users in Changchun Municipality, Northeast China

Cited by 8 publications

References 22 publications

City-scale synthetic individual-level vehicle trip data

City-scale synthetic individual-level vehicle trip data

Use of mobile digital footprint to understand how COVID-19 impacts the visitors to art places in Singapore

Dynamic Community Detection Based on a Label-Based Swarm Intelligence

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