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.

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 COLLABORATIVE LEARNING BY BOOSTING IN DISTRIBUTED ENVIRONMENTS

2010 ◽  
Vol 24 (05) ◽  
pp. 763-789
Author(s):  
SHIJUN WANG ◽  
CHANGSHUI ZHANG
Keyword(s):  
Network Topology ◽  
Distributed Learning ◽  
Real Data ◽  
Distributed Applications ◽  
Data Sets ◽  
Human Society ◽  
Scale Free ◽  
Comparison Results ◽  
Algorithm Comparison ◽  
Training Error

In human society, people learn from each other and knowledge is accumulated from generation to generation. This provides some hints to distributed learning. For distributed applications, each site has its own data. If we can build a local model for each site and improve the model based on models learned by its neighbor sites with low communication cost, then it would be very helpful to the distributed applications. In this paper, we propose a new distributed learning method called distributed network boosting (DNB) algorithm for distributed applications. The learned hypotheses are exchanged between neighboring sites during learning process. Theoretical analysis shows that the DNB algorithm minimizes the cost function through collaborative functional gradient descent in hypotheses space. We also give upper bounds of training error and generalization error of the DNB algorithm. Comparison results of the DNB algorithm with other algorithms on real data sets with different sizes show the effectiveness of the proposed algorithm for distributed applications. In order to show the influence of network topology on the performance of the DNB algorithm, we tested it on random graphs and scale-free networks. Bias-variance decomposition shows that the network topology plays an important role in controlling the diversity of the learned classifier ensemble.

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

scholarly journals Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3550 ◽  
Author(s):  
Juan José Vinagre Díaz ◽  
Rubén Fernández Pozo ◽  
Ana Belén Rodríguez González ◽  
Mark R. Wilby ◽  
Carmen Sánchez Ávila
Keyword(s):  
Real Data ◽  
Mobility Model ◽  
Edge Computing ◽  
Agglomerative Clustering ◽  
Residential Areas ◽  
The Public ◽  
Sensor Platform ◽  
Hierarchical Agglomerative Clustering ◽  
Spatio Temporal ◽  
Geographical Maps

Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies transporting bicycles to and from docking stations in order to guarantee the service. Rebalancing performs clustering to group docking stations by behaviour and proximity. In this paper we propose a Hierarchical Agglomerative Clustering based on an Ultra-Light Edge Computing Algorithm (HAC-ULECA). We eliminate the proximity and let Hierarchical Agglomerative Clustering (HAC) focus on behaviour. Behaviour is represented by ULECA as an activity profile based on the net flow of arrivals and departures in a docking station. This drastically reduces the computing requirements which allows ULECA to run as an edge computing functionality embedded into the physical layer of the Internet of Shared Bikes (IoSB) architecture. We have applied HAC-ULECA to real data from BiciMAD, the public BSS in Madrid (Spain). Our results, presented as dendograms, graphs, geographical maps, and colour maps, show that HAC-ULECA is capable of separating behaviour profiles related to business and residential areas and extracting meaningful spatio-temporal information about the BSS and the city’s mobility.

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.

Fitting real data by means of non-homogeneous log-normal diffusion processes

2017 ◽  
Vol 10 (4) ◽  
pp. 585-600 ◽  
Author(s):  
Patricia Román-Román ◽  
Juan José Serrano-Pérez ◽  
Francisco Torres-Ruiz
Keyword(s):  
Diffusion Processes ◽  
Real Data ◽  
Normal Diffusion ◽  
Log Normal

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

Effect of triangle behavior on topological properties of weighted networks

2019 ◽  
Vol 33 (16) ◽  
pp. 1950170
Author(s):  
Qi An ◽  
Yajing Zhou ◽  
Kehua Chen ◽  
Weilong Chen ◽  
Guan Yan ◽  
...  
Keyword(s):  
Significant Influence ◽  
Transmission Rate ◽  
Strength Distribution ◽  
Topological Properties ◽  
Epidemic Spreading ◽  
The Real ◽  
Scale Free ◽  
Community Model ◽  
New Community ◽  
Theoretical Results

As an essential dynamic evolving mechanism, triangle behavior can be observed ubiquitously in the real world. Combining transfer mechanism with weighted dynamic, in this paper, we propose a new community model and deduce the strength distribution. Consistent with the theoretical results, numerical simulations show decent right-skewed scale-free characters of strength distribution. Moreover, we calculate some important coefficients to analyze the correlations of nodes and demonstrate the disassortative property of this model. Finally, the process of epidemic spreading with different weighted transmission rate is introduced on the weighted community network, and the results indicate that the triangle behavior has a significant influence on the dynamic of the epidemic spreading. Composed with the models proposed already, our model is supposed to be closer to the realistic network and imitate the real system more accurately and exactly.

scholarly journals Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data

2018 ◽  
Vol 7 (12) ◽  
pp. 459 ◽  
Author(s):  
Xiaoyi Zhang ◽  
Wenwen Li ◽  
Feng Zhang ◽  
Renyi Liu ◽  
Zhenhong Du
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Visual Analytics ◽  
Human Mobility ◽  
Residential Areas ◽  
Trip Purpose ◽  
Mobility Data ◽  
Point Of Interest ◽  
Functional Zones ◽  
Urban Functions

Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However, very few works have leveraged public bicycle-sharing data, which provides a useful feature in depicting people’s short-trip transportation within a city, in the studies of urban functions and structure. Because of its convenience, bicycle usage tends to be close to point-of-interest (POI) features, the combination of which will no doubt enhance the understanding of the trip purpose for characterizing different functional zones. In our study, we propose a data-driven approach that uses station-based public bicycle rental records together with POI data in Hangzhou, China to identify urban functional zones. Topic modelling, unsupervised clustering, and visual analytics are employed to delineate the function matrix, aggregate functional zones, and present mixed land uses. Our result shows that business areas, industrial areas, and residential areas can be well detected, which validates the effectiveness of data generated from this new transportation mode. The word cloud of function labels reveals the mixed land use of different types of urban functions and improves the understanding of city structures.

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.

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