A dynamic multi-dimensional conceptual data model for transportation applications

Demirel, Hande

doi:10.1016/j.isprsjprs.2004.02.002

Cited by 12 publications

(3 citation statements)

References 17 publications

(20 reference statements)

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“…A spatiotemporal data warehouse is a geometric and time-varying collection of data that supports spatiotemporal OLAP operations for extraction of spatiotemporal information (2)(3)(4)(5)(6)(7)(8). In the near future, spatiotemporal data warehousing will play a crucial role in assisted decision making for such applications as traffic supervision systems, transportation management (9), and digital battlefields.…”

Section: Spatiotemporal Data Warehouse For Vehicle Supervisionmentioning

confidence: 99%

Spatiotemporal Data Warehouse for Vehicle Supervision

Zhou

Cheng-zhi

et al. 2013

Transportation Research Record

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The large amounts of spatiotemporal data generated by vehicle supervision systems cannot be efficiently managed by ordinary databases, mainly because of long query responses. To overcome the limitations of ordinary databases, this paper proposes a new approach, the grid time (GT)–indexed cube, which is a spatial grid–indexed, adaptive grid-based, trajectory-supported warehouse for spatiotemporal data. The GT cube partitions an embedded space–time into a set of size-fixed grids to form a cube that continues to grow throughout a constant time interval. Each grid is assigned an identifier composed of its coordinates and start time, and an aggregated value for each grid is stored in the grid records, regardless of the temporal length of the queries. Additionally, the basic grid structure of the GT cube remains unchanged at each time interval. Instead, this method refines the grid in a selected region to handle data skewing by adaptively partitioning the grid into subgrids. After extensive performance studies were conducted with spatiotemporal data from the main vehicle supervision system of Guangdong Province, China, it was observed that the GT cube achieved higher query performance than ordinary data storage technologies under various operational conditions, was easily applicable in practice, and demonstrated compatibility with traditional databases.

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Section: Spatiotemporal Data Warehouse For Vehicle Supervisionmentioning

confidence: 99%

Spatiotemporal Data Warehouse for Vehicle Supervision

Zhou

Cheng-zhi

et al. 2013

Transportation Research Record

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“…Since the work of Claramunt et al (2000) who propose a "very dynamic" GIS that integrates static urban data with dynamic traffic flows to monitor urban traffic, other contributions have explored the potential of GIS in performing temporal-based analysis for studying land use and transportation interactions and other transportation applications (Shaw and Xin, 2003;Demirel, 2004;Yu, 2007;Ahmed and Miller, 2007).…”

Section: 3geomodelling Framework In Transportation Researchmentioning

confidence: 99%

Geo-ICT in Transportation Science

Borzacchiello¹,

Casas

Ciuffo³

et al. 2009

Geospatial Technology and the Role of Location in Science

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Since the first appearances of Geographical Information Systems (GIS), transportation science has found in them its natural support, to represent, first, the spatial and, later on, the temporal aspects of transportation networks and infrastructures. As GIS developed so did their use in transportation science, and they became essential not only in visualization but also to facilitate and speed data management, algorithmic operations, and decision making. This paper analyses the contributions and influence of GIS in transport science on the basis of three frameworks: geodatabase, geomapping, and geomodelling, all of which highlight the importance of location.

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“…However, the model remained at the conceptual level; moreover, the implementation issues were still confusing. Demirel (2004b) proposed a dynamic multi-dimensional (3D geometry and time) data model that involved multi-dimensional location referencing and multi-scale representation. This model aimed to decompose geometry, topology and non-spatial data.…”

mentioning

confidence: 99%

Hierarchical lane‐oriented 3D road‐network model

Zhu

2008

International Journal of Geographical Information Science

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The existing road-network models based on the 2D link-node of roadway centrelines have inhibited lane-oriented network flow analysis and multidimensional inventory management in complicated 3D urban environments. This paper proposes a hierarchical lane-oriented 3D road-network model (HL-3DRNM), with a unified modelling language (UML) diagram. HL-3DRNM is a non-planar topological model with the support of a 3D lane ribbon cartographic display, which is characterized by: (1) multiple topological and cartographic representations and various abstraction levels (street, road segment, carriageway and lane); and (2) referenced multi-dimensional road information (point, line, area and volume) at lane level. HL-3DRNM provides solid mathematical foundations for a more detailed inventory management, effective network analysis and realistic navigation in the increasingly complicated 3D urban transportation systems.

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A dynamic multi-dimensional conceptual data model for transportation applications

Cited by 12 publications

References 17 publications

Spatiotemporal Data Warehouse for Vehicle Supervision

Spatiotemporal Data Warehouse for Vehicle Supervision

Geo-ICT in Transportation Science

Hierarchical lane‐oriented 3D road‐network model

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