scholarly journals MIDET: A Method for Indexing Traffic Events

2021 ◽  
Author(s):  
Mariana M. G. Duarte ◽  
Marcos V. Pontarolo ◽  
Rebeca Schroeder ◽  
Carmem S. Hara
Keyword(s):  
Traffic Control ◽  
Web Applications ◽  
Grid Cell ◽  
Real Data ◽  
Index Structure ◽  
Temporal Data ◽  
Bitmap Index ◽  
Traffic Lights ◽  
Spatio Temporal ◽  
Control Traffic

Traffic events announcements such as jams and road closures are continuously reported by mobile and Web applications. This collection of spatio-temporal data is an important source of information for urban planning, and can be used to orchestrate a number of actions to mprove the mobility, such as traffic control, traffic lights synchronization and preventive maintenance. Such analysis usually involves computation of spatial relationships among data, and may involve location of landmarks, roads and different types of events. In this paper, we propose a Method for Indexing Traffic Events (MIDET) for querying spatio-temporal data, whose location can be represented as a point or collection of points. MIDET is based on a fixed-grid space-oriented partitioning. In order to tackle the data skew, each grid cell is associated with a set of blocks containing event records. Moreover, a bitmap index is used for filtering out blocks without retrieving the actual data. MIDET provides the following benefits: adoption of a simple bulk loading process to manage dynamic insertion streams, and in-memory spatial joins. We conducted an experimental study using real data obtained from Waze. MIDET’s query performance was compared with Postgis, which adopts an R-tree index structure.

Human Movement Analysis Using Heterogeneous Data Sources

2013 ◽  
Vol 4 (3) ◽  
pp. 98-117 ◽  
Author(s):  
João Peixoto ◽  
Adriano Moreira
Keyword(s):  
Intelligent Transportation Systems ◽  
Real Data ◽  
Human Movement ◽  
Heterogeneous Data ◽  
Transportation Systems ◽  
Temporal Data ◽  
Urban Mobility ◽  
Multiple Sources ◽  
Urban Dynamics ◽  
Spatio Temporal

The analysis of urban mobility has been attracting the interest of the research community recently. The research challenges in this domain are diverse and include data acquisition and representation, human movement modeling and the visualization of dynamic geo-referenced data. Some of the direct applications for these studies are urban planning, security, intelligent transportation systems and wireless networks optimization. One of the drivers for recent work in this area is the availability of large datasets representing many aspects of the urban dynamics. Quite often, the proposed approaches are highly dependent on the data type. However, the analysis of urban dynamics could benefit from the combined and simultaneous use of multiple sources of spatio-temporal data. This paper describes the definition of a set of basic concepts for the representation and processing of spatio-temporal data, sufficiently flexible to deal with various types of mobility data and to support multiple forms of processing and visualization of the urban mobility. For this purpose the authors define a set of concepts and describe how real data from heterogeneous sources is mapped into the proposed framework. Available results obtained by the integration of geometric and symbolic data reveal the adequacy of the proposed concepts, and uncover new possibilities for the fusion of heterogeneous datasets.

scholarly journals A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

2021 ◽  
Author(s):  
Shengnan Ke ◽  
Jun Gong ◽  
Songnian Li ◽  
Qing Zhu ◽  
Xintao Liu ◽  
...  
Keyword(s):  
Hash Table ◽  
Index Structure ◽  
Hybrid Index ◽  
Temporal Data ◽  
Trajectory Data ◽  
Generation Efficiency ◽  
Tree Structures ◽  
Data Indexing ◽  
Indexing Method ◽  
Spatio Temporal

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

scholarly journals Spatially multi-scale dynamic factor modeling via sparse estimation

2019 ◽  
Vol 11 (01) ◽  
pp. 1950005
Author(s):  
Takamitsu Araki ◽  
Shotaro Akaho
Keyword(s):  
Factor Model ◽  
Real Data ◽  
Factor Loading ◽  
Temporal Variations ◽  
Spatial Variations ◽  
Dynamic Factor ◽  
Temporal Data ◽  
Sparse Estimation ◽  
Local Structures ◽  
Spatio Temporal

In many spatio-temporal data, their spatial variations have inherent global and local structures. The spatially continuous dynamic factor model (SCDFM) decomposes the spatio-temporal data into a small number of spatial and temporal variations, where the spatial variations are represented by the factor loading (FL) functions. However, the FL functions estimated by the maximum likelihood or maximum [Formula: see text] penalized likelihood capture global structures but do not capture local structures. We propose a method for estimating the spatially multiscale FL functions using a sparse penalty. To overcome the problems of existing sparse penalties, we propose the adaptive graph lasso (AGL) penalty. The method with the AGL penalty eliminates redundant basis functions contained in the FL functions, and leads to the FL functions having global and localized structures. We derive the EM algorithm with block coordinate descent that enables us to maximize the AGL penalized log-likelihood stably. Applications to synthetic and real data show that the proposed modeling procedure accurately extract not only the spatially global structures but also spatially local structures, which the [Formula: see text] penalized estimation do not extract.

Topography and displacement of polar glaciers from multi-temporal SAR interferograms

Polar Record ◽  
2007 ◽  
Vol 43 (4) ◽  
pp. 331-343 ◽  
Author(s):  
Franz J. Meyer
Keyword(s):  
Real Data ◽  
Data Sets ◽  
Temporal Data ◽  
Error Sources ◽  
Processing Step ◽  
Multi Temporal ◽  
Least Squares Adjustment ◽  
Temporal Model ◽  
Spatio Temporal ◽  
A New Technique

ABSTRACTThis paper describes a new technique simultaneously to estimate topography and motion of polar glaciers from multi-temporal SAR interferograms. The approach is based on a combination of several SAR interferograms in a least-squares adjustment using the Gauss-Markov model. For connecting the multi-temporal data sets, a spatio-temporal model is proposed that describes the properties of the surface and its temporal evolution. Rigorous mathematical modelling of functional and stochastic relations allows for a systematic description of the processing chain. It is also an optimal tool to set parameters for the statistics of every individual processing step, and the propagation of errors into the results. Within the paper theoretical standard deviations of the unknowns are calculated depending on the configuration of the data sets. The influence of gross errors in the observations and the effect of non-modelled error sources on the unknowns are estimated. A validation of the approach based on real data concludes the paper.

scholarly journals A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

2021 ◽  
Author(s):  
Shengnan Ke ◽  
Jun Gong ◽  
Songnian Li ◽  
Qing Zhu ◽  
Xintao Liu ◽  
...  
Keyword(s):  
Hash Table ◽  
Index Structure ◽  
Hybrid Index ◽  
Temporal Data ◽  
Trajectory Data ◽  
Generation Efficiency ◽  
Tree Structures ◽  
Data Indexing ◽  
Indexing Method ◽  
Spatio Temporal

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

Motion data index structure: an efficient indexing for spatio-temporal data of moving objects

2005 ◽  
Author(s):  
Huanzhuo Ye ◽  
Hongxia Luo ◽  
Jianya Gong ◽  
Lu Zhang ◽  
Yan Wang
Keyword(s):  
Moving Objects ◽  
Index Structure ◽  
Temporal Data ◽  
Motion Data ◽  
Spatio Temporal

scholarly journals Intelligent Traffic Signaling Control Using Petri Nets

2020 ◽  
Vol 3 (1) ◽  
pp. 1-13
Author(s):  
Anas A. M. HARB ◽  
Hakan TERZİOĞLU ◽  
Akif DURDU
Keyword(s):  
Petri Nets ◽  
Traffic Control ◽  
Classical Method ◽  
Real Data ◽  
Fixed Time ◽  
Traffic Lights ◽  
Traffic System ◽  
Surrounding Areas ◽  
Signalling Systems ◽  
Study Application

Transportation vehicles refer to the vehicles used to transport people's needs and goods from one area to another. Intelligent traffic signalling systems are needed due to the increase in the number of vehicles in traffic, the roads used, the insufficiency of the surrounding areas and the inability to widen these roads or construct upper / lower roads. Nowadays, there are situations where traffic control is insufficient in current density due to the traffic system mechanisms being dependent on fixed time. Therefore, some smart methods have been developed to prevent waste of time and economic problems. In this study, application of Petri Nets (PA) based on real data collected at certain times of the day for intelligent control of traffic lights at a four-way intersection. PA's performances have been compared with their classical (fixed time) performances. When the results obtained were evaluated in terms of both the number of passing vehicles and time, it was seen that the PA passed 849 vehicles in 755 seconds, 341 vehicles in 920 seconds in the classical method and a better result was obtained with PA.

scholarly journals Efficient Historical Query in HBase for Spatio-Temporal Decision Support

2016 ◽  
Vol 11 (5) ◽  
pp. 613 ◽  
Author(s):  
Xiaoying Chen ◽  
Chong Zhang ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Decision Support ◽  
Storage System ◽  
Range Query ◽  
Index Structure ◽  
Temporal Data ◽  
Effective Solution ◽  
Nosql Database ◽  
Knn Query ◽  
Spatio Temporal ◽  
Analysis Platform

Comparing to last decade, technologies to gather spatio-temporal data are more and more developed and easy to use or deploy, thus tens of billions, even trillions of sensed data are accumulated, which poses a challenge to spatio-temporal Decision Support System (stDSS). Traditional database hardly supports such huge volume, and tends to bring performance bottleneck to the analysis platform. Hence in this paper, we argue to use NoSQL database, HBase, to replace traditional back-end storage system. Under such context, the well-studied spatio-temporal querying techniques in traditional database should be shifted to HBase system parallel. However, this problem is not solved well in HBase, as many previous works tackle the problem only by designing schema, i.e., designing row key and column key formation for HBase, which we don’t believe is an effective solution. In this paper, we address this problem from nature level of HBase, and propose an index structure as a built-in component for HBase. STEHIX (Spatio-TEmporal Hbase IndeX) is adapted to two-level architecture of HBase and suitable for HBase to process spatio-temporal queries. It is composed of index in the meta table (the first level) and region index (the second level) for indexing inner structure of HBase regions. Base on this structure, three queries, range query, kNN query and GNN query are solved by proposing algorithms, respectively. For achieving load balancing and scalable kNN query, two optimizations are also presented. We implement STEHIX and conduct experiments on real dataset, and the results show our design outperforms a previous work in many aspects.

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