scholarly journals Semantic integration of urban mobility data for supporting visualization

2017 ◽  
Vol 24 ◽  
pp. 180-188 ◽  
Author(s):  
Thiago Sobral ◽  
Teresa Galvão ◽  
José Borges
Keyword(s):  
Semantic Integration ◽  
Urban Mobility ◽  
Mobility Data

Profiling rapid urban transformation through urban mobility data in Shenzhen

2020 ◽  
pp. 86-100
Author(s):  
Chen Zhong ◽  
Zhaoliang Luan ◽  
Yao Shen ◽  
Xiaoming Li ◽  
Wei Tu
Keyword(s):  
Urban Transformation ◽  
Urban Mobility ◽  
Mobility Data

scholarly journals How do urban mobility (geo)graph’s topological properties fill a map?

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Leonardo Bacelar Lima Santos ◽  
Luiz Max Carvalho ◽  
Wilson Seron ◽  
Flávio C. Coelho ◽  
Elbert E. Macau ◽  
...  
Keyword(s):  
Traffic Engineering ◽  
Real Data ◽  
Urban Mobility ◽  
Topological Properties ◽  
Residential Areas ◽  
Scale Free ◽  
Mobility Data ◽  
Open Source Gis ◽  
Log Normal ◽  
Flow Threshold

Abstract Urban mobility data are important to areas ranging from traffic engineering to the analysis of outbreaks and disasters. In this paper, we study mobility data from a major Brazilian city from a geographical viewpoint using a Complex Network approach. The case study is based on intra-urban mobility data from the Metropolitan area of Rio de Janeiro (Brazil), presenting more than 480 spatial network nodes. While for the mobility flow data a log-normal distribution outperformed the power law, we also found moderate evidence for scale-free and small word effects in the flow network’s degree distribution. We employ a novel open-source GIS tool to display (geo)graph’s topological properties in maps and observe a strong traffic-topology association and also a fine adjustment for hubs location for different flow threshold networks. In the central commercial area for lower thresholds and in high population residential areas for higher thresholds. This set of results, including statistical, topological and geographical analysis may represent an important tool for policymakers and stakeholders in the urban planning area, especially by the identification of zones with few but strong links in a real data-driven mobility network.

scholarly journals An Overview of Indicators and Indices Used for Urban Mobility Assessment

2019 ◽  
Vol 31 (6) ◽  
pp. 703-714 ◽  
Author(s):  
Krešimir Vidović ◽  
Marko Šoštarić ◽  
Damir Budimir
Keyword(s):  
Urban Areas ◽  
Assessment Process ◽  
Urban Mobility ◽  
Dimensional Approach ◽  
Global Trends ◽  
Mobility Data ◽  
Mobility Assessment ◽  
Local Specifications ◽  
Layered Approach ◽  
Definition Of

The urban mobility is affected by global trends resulting in a growing passenger and freight transport demand. In order to improve the understanding of urban mobility in general, to evaluate mobility services and to quantify the overall transport system performance, it is necessary to assess urban mobility. Urban mobility assessment requires the application of methodology integrating different metrics and explicitly applying a multi-dimensional approach. Since scientific community does not define urban mobility in an unambiguous way, part of this paper is devoted to the analysis of the definition of urban mobility. This step enables better understanding of urban mobility in general, as well as understanding of the urban mobility assessment process. Usually, a three-layered approach that includes urban mobility data, indicators and indices is used for the assessment. Therefore, the aim of this paper was to perform extensive research in order to synthesize, define and organize the elements of those layers. The existing urban mobility indicators and indices have been developed for specific urban areas, taking into account local specifications, and they are not applicable in other cities. Also, the choice of urban mobility indicators is mainly related to the existence of data sources, which limits the objective and comparable assessment of the mobility of cities where such data do not exist.

scholarly journals OBAVUM: An ontology-based approach to visualizing urban mobility data

2016 ◽  
Author(s):  
Thiago Sobral ◽  
Vera Costa ◽  
Jose Borges ◽  
Tania Fontes ◽  
Teresa Galvao
Keyword(s):  
Urban Mobility ◽  
Mobility Data

scholarly journals Visualization of Urban Mobility Data from Intelligent Transportation Systems

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 332 ◽  
Author(s):  
Thiago Sobral ◽  
Teresa Galvão ◽  
José Borges
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Urban Mobility ◽  
Multiple Perspectives ◽  
Data Types ◽  
Mobility Data ◽  
Extensive Body ◽  
Visualization Techniques

Intelligent Transportation Systems are an important enabler for the smart cities paradigm. Currently, such systems generate massive amounts of granular data that can be analyzed to better understand people’s dynamics. To address the multivariate nature of spatiotemporal urban mobility data, researchers and practitioners have developed an extensive body of research and interactive visualization tools. Data visualization provides multiple perspectives on data and supports the analytical tasks of domain experts. This article surveys related studies to analyze which topics of urban mobility were addressed and their related phenomena, and to identify the adopted visualization techniques and sensors data types. We highlight research opportunities based on our findings.

scholarly journals What can urban mobility data reveal about the spatial distribution of infection in a single city?

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Robert Moss ◽  
Elham Naghizade ◽  
Martin Tomko ◽  
Nicholas Geard
Keyword(s):  
Spatial Distribution ◽  
Urban Mobility ◽  
Mobility Data

Urban mobility data to support the assessment of dynamic vulnerability to disasters

2015 ◽  
Author(s):  
Luciana de Resende Londe ◽  
Leonardo Bacelar Lima Santos ◽  
Erico Soriano ◽  
Livia Rodrigues Tomas ◽  
Tiago Carvalho
Keyword(s):  
Urban Mobility ◽  
Mobility Data

scholarly journals Inferring Urban Land Use from Multi-Source Urban Mobility Data Using Latent Multi-View Subspace Clustering

2021 ◽  
Vol 10 (5) ◽  
pp. 274
Author(s):  
Qiliang Liu ◽  
Weihua Huan ◽  
Min Deng ◽  
Xiaolin Zheng ◽  
Haotao Yuan
Keyword(s):  
Land Use ◽  
Subspace Clustering ◽  
Urban Land ◽  
Urban Land Use ◽  
Single Type ◽  
Underlying Structure ◽  
Urban Mobility ◽  
Trajectory Data ◽  
Mobility Data ◽  
Land Use Types

In the era of big data, vast urban mobility data introduce new opportunities to infer urban land use from the perspective of social function. Most existing works only derive land use information from a single type of urban mobility dataset, which is typically biased and results in difficulty obtaining a comprehensive view of urban land use. It remains challenging to fuse high-dimensional and noisy multi-source urban mobility data to infer urban land use. This study aimed to infer urban land use from multi-source urban mobility data using latent multi-view subspace clustering. The variation in the number of origin/destination points over time was initially used to characterize land use types. Then, a latent multi-view representation was applied to construct the common underlying structure shared by multi-source urban mobility data and effectively deal with noise. Finally, based on the latent multi-view representation, the subspace clustering method was used to infer the land use types. Experiments on taxi trajectory data and bus smart card data in Beijing reveal that, compared with the method using a single type of urban mobility dataset and the weighted fusion method, the approach presented in this study obtains the highest detection rate of land use. The urban land use inferred in this study provides calibration and reference for urban planning.

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