Mining GPS data for mobility patterns: A survey

Lin, Mingzhu; Hsu, Wen-Jing

doi:10.1016/j.pmcj.2013.06.005

Cited by 141 publications

(94 citation statements)

References 68 publications

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“…Geometric Distance [9,10,19,27,[30][31][32]34,44,45] Direction [8,[16][17][18]23,27,33,34,39] Turning Angle [19,23,28,31] Sinuosity [16,20,28,31] Physical Velocity [8,18,19,27,28,44] Acceleration [18][19][20] Stop Point [17,18,27,30,33,34,36,43] POI [9,10,[14][15][16][21][22][23][24]…”

Section: Parameters Related Workmentioning

confidence: 99%

“…In order to improve processing times, a given trajectory should be filtered by keeping the most relevant points, according to the most relevant geometric descriptors. To this end, several algorithms have already been explored using spatial and temporal descriptors, such as turning points, directions, sinuosity, and speed [5,19,31]. A key issue when applying a filtering algorithm to a given trajectory is identifying the most relevant geometric descriptors, the ones that make sense with respect to the application domain considered, as well as avoiding dependent parameters.…”

Section: Parameters Related Workmentioning

confidence: 99%

“…Among the variety of processes that have been developed so far, clustering, Origin-Destination (O-D) methods, spatio-temporal mining, and graph-based analysis are representative trajectory analyses that Similarity-based analysis is rather oriented to the detection of trajectory analogies and peculiarities [31]. Over the past few years, the analysis and search for similarities based on physical and geometric descriptors has been the object of several research works.…”

Section: Introductionmentioning

confidence: 99%

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A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

Milaghardan

Abbaspour

Claramunt

2018

IJGI

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Large volumes of trajectory-based data require development of appropriate data manipulation mechanisms that will offer efficient computational solutions. In particular, identification of meaningful geometric points of such trajectories is still an open research issue. Detection of these critical points implies to identify self-intersecting, turning and curvature points so that specific geometric characteristics that are worth identifying could be denoted. This research introduces an approach called Trajectory Critical Point detection using Convex Hull (TCP-CH) to identify a minimum number of critical points. The results can be applied to large trajectory data sets in order to reduce storage costs and complexity for further data mining and analysis. The main principles of the TCP-CH algorithm include computing: convex areas, convex hull curvatures, turning points, and intersecting points. The experimental validation applied to Geolife trajectory dataset reveals that the proposed framework can identify most of intersecting points in reasonable computing time. Finally, comparison of the proposed algorithm with other methods, such as turning function shows that our approach performs relatively well when considering the overall detection quality and computing time.

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Section: Parameters Related Workmentioning

confidence: 99%

Section: Parameters Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

Milaghardan

Abbaspour

Claramunt

2018

IJGI

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“…This information about individual's mobility behavior is granting more accurate mobility pattern studies since more trajectory areas and people can be covered. These mobility data have been used for different applications like traffic prediction and city's planning, as well as analyzed using data mining [61].…”

Section: Data Mining Of Geotagged Messagesmentioning

confidence: 99%

Urban Analysis and Smart Communities: An Approach to the Use of Technology in Everyday Mobility

Maury

Vilas

2015

Inge CUC

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Abstract-Concentration of population in urban centers is a global problem for which different strategies in order to organize different processes in cities and improve the quality of life are required. The creation of smart communities is shown as a sustainable solution since they deal with various key aspects, such as traffic management and mobility, through the use of information technologies (ITs). This work presents a review of recent studies using information technologies for urban analysis and mobility in cities. A descriptive analysis of automated methods for collecting and analyzing citizens' mobility patterns is performed; it is centered in smart card use, geolocation and geotagging. It is concluded that a robust communication infrastructure, supported by an efficient computational platform allowing big data management and ubiquitous computing, is a crucial aspect for urban management in a smart community.Keywords--Smart Community, Urban Analysis, Pattern Mobility, Smart Cards, Geolocation, Geotagging.Resumen--La concentración de la población en los centros urbanos es una problemática mundial que requiere de estrategias que permitan organizar sus procesos y mejorar la calidad de vida. La creación de comunidades inteligentes se muestra como una solución sostenible, debido a que éstas trabajan aspectos claves para el desarrollo urbano, como la gestión de tráfico y la movilidad, apoyada en las tecnologías de la información (TICs). Este trabajo presenta una revisión del estado del arte en cuanto a la aplicación de las TICs al análisis urbano y movilidad ciudadana. Se analizan descriptivamente diversos métodos automáticos para la recolección y el análisis del patrón de movilidad de los ciudadanos, enfocándose en el uso de tarjetas inteligentes, geolocalización y geoetiquetado. Se encuentra que una infraestructura de comunicaciones robusta, apoyada en una plataforma computacional ágil con manejo de grandes datos y computación ubicua, es primordial para la gestión urbana en una comunidad inteligente.

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“…DBSCAN results use the density of position data as the cluster-forming criteria, while k-Means use the parameter k to split the set of position data into k clusters. Note that DBSCAN clustering results shape follows the position data shape, which is useful in numerous scenarios, for example, to discover a cluster of persons waiting in line for a music concert [44]. Due to its highest accuracy, DBSCAN is considered a benchmark of clustering algorithms.…”

Section: Spatial Clusteringmentioning

confidence: 99%

Dg2cep: An on-Line Algorithm for Real-Time Detection of Spatial Clusters From Large Data Streams Through Complex Event Processing

RORIZ¹

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Mining GPS data for mobility patterns: A survey

Cited by 141 publications

References 68 publications

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

Urban Analysis and Smart Communities: An Approach to the Use of Technology in Everyday Mobility

Dg2cep: An on-Line Algorithm for Real-Time Detection of Spatial Clusters From Large Data Streams Through Complex Event Processing

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