Extracting Significant Mobile Phone Interaction Patterns Based on Community Structures

Ghahramani, Mohammadhossein; Zhou, MengChu; Hon, Chi Tin

doi:10.1109/tits.2018.2836800

Cited by 43 publications

(18 citation statements)

References 30 publications

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“…Distance-based clustering methods [41], such as K-means, Gaussian Mixture Models, Spectral Clustering, and Density-Based Spatial Clustering as well as some recent improvements [42], [43] determine the centroids by minimizing the distance of the samples and centroids. However, the above methods may be ineffective when the input data are of high dimensionality and complex coupling, also known as the curse of dimensionality.…”

Section: A Problem Description and Review Of Existing Methodsmentioning

confidence: 99%

A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Section: A Problem Description and Review Of Existing Methodsmentioning

confidence: 99%

A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…Finally, renewable energy sources for power generation are explored as a viable solution for CO2 emission reduction in Africa. Spatiotemporal approaches [10,11,12,13,14] can be utilized to explore CO2 emissions. Several studies have analyzed emissions based on spatial decomposition methods [15,16].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Carbon Dioxide Emissions From Road Transport Using Taxi Trips

Ghahramani

Pilla

2021

IEEE Access

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Transport emissions, including road, rail, air, and marine transportation, account for a large part of the overall emissions; hence, there is a need to review strategies for managing associated issues and coping with negative impacts. A simultaneous improvement in economic efficiency can help us achieve our desired objectives in the concerned context. Sharing economy, i.e., a peer-to-peer-based sharing of access to assets, can help reduce the total resources required and consequently reduce carbon footprints. In line with this objective, we propose an intelligent model to study carbon dioxide emissions from road transport using taxi trips in Dublin, Ireland. The proposed method is a hybrid unsupervised learning approach tailored for the particular structure of the problem. We present how an intelligent approach can be implemented to model CO2 emissions from road transport. The model categorizes taxis based on different features related to the emissions they release. Different clusters are detected. Those vehicles labeled as highest emitters can be targeted for further improvements in reducing CO2, i.e., replacing pollutant cars with electric cars or including them in the taxi fleet as sharing ones only.

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“…ese studies validate the feasibility of using geospatial data to analyze the spatial-temporal features of urban travel patterns. Ghahramani et al have explored the potential of using mobile phone data to study the inter and intra-interaction patterns of the urban community structure and identify activity hotspots, while they did not consider the overlapping community structure of urban interaction patterns [40][41][42].…”

Section: Related Workmentioning

confidence: 99%

Mobile Phone Data in Urban Commuting: A Network Community Detection-Based Framework to Unveil the Spatial Structure of Commuting Demand

Liu

Yang

et al. 2020

Journal of Advanced Transportation

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As the outcomes of rapid urbanization, the spatial separation of homes and workplaces extends the commuting distance and complicates the commuting demand of residents. To promote urban livability and sustainability, it becomes crucially important to understand the commuting patterns by decomposing and simplifying the diverse commuting demand. In this paper, a methodology framework is proposed to describe the spatial structure of commuting demand in a city using mobile phone data. Four steps are mainly included in the proposed methodology: the preprocessing of mobile phone data, the labeling of individuals and their activity points, the construction of the jobs-housing relationship network, and the network decomposition based on the community detection algorithm. To demonstrate the practical use of the proposed methodologies, a case study is carried out in Shanghai to explore the commuting patterns of Shanghai residents. The result indicates the regions with dense jobs-housing connections and cross-regional commuting demand. The result also finds that the administrative boundaries show a significant effect on the residential commuting behavior and the metro lines on the cross-regional commuting behavior. The results generated by the methodology proposed can be referenced by policymakers to support urban transportation planning and promote urban livability and sustainability.

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Extracting Significant Mobile Phone Interaction Patterns Based on Community Structures

Cited by 43 publications

References 30 publications

A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation

A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation

Analysis of Carbon Dioxide Emissions From Road Transport Using Taxi Trips

Mobile Phone Data in Urban Commuting: A Network Community Detection-Based Framework to Unveil the Spatial Structure of Commuting Demand

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