scholarly journals An Adaptive Parallel Method for Indexing Transportation Moving Objects

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kun-lun Chen ◽  
Chuan-wen Li ◽  
Guang Lu ◽  
Jia-quan Li ◽  
Tong Zhang
Keyword(s):  
Traffic Control ◽  
Moving Objects ◽  
Skewed Distribution ◽  
Grid Data ◽  
Quad Tree ◽  
Indexing Method ◽  
Object Databases ◽  
High Level ◽  
Data Updating ◽  
Moving Object Databases

Transportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. To address this problem and improve the throughput of moving object databases, we propose a GPU-accelerated indexing method, based on a grid data structure, combined with quad-trees. We count object movements and decide whether a particular node should be split or be merged on the GPU. In this case, bottlenecked nodes can be translated to quad-tree without interfering with the CPU. Hence, waiting time of other threads caused by locking operations raised by object data updating can be reduced. The method is simple while more adaptive to scenarios where the distribution of moving objects is skewed. It also avoids shortcomings of existing methods with performance bottleneck on the hot area or spending plenty of calculation resources on structure balancing. Experiments suggest that our method shows higher throughput and lower response time than the existing indexing methods. The advantage is even more significant under the skewed distribution of moving objects.

A Survey of Spatio-Temporal Data Warehousing

2010 ◽  
pp. 949-977
Author(s):  
Leticia Gómez ◽  
Bart Kuijpers ◽  
Bart Moelans ◽  
Alejandro Vaisman
Keyword(s):  
Moving Objects ◽  
Route Planning ◽  
Geographic Information ◽  
Moving Object ◽  
Attribute Data ◽  
Object Databases ◽  
Spatio Temporal ◽  
Eu Project ◽  
The University ◽  
Moving Object Databases

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community. However, not much has been done in providing moving object databases with OLAP functionality. In the first part of this article we survey the SOLAP literature. We then move to Spatio-Temporal OLAP, in particular addressing the problem of trajectory analysis. We finally provide an in-depth comparative analysis between two proposals introduced in the context of the GeoPKDD EU project: the Hermes-MDC system, and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina.

U-Tree Based Indexing Method for Uncertain Moving Objects

2008 ◽  
Vol 19 (10) ◽  
pp. 2696-2705 ◽  
Author(s):  
Xiao-Feng DING ◽  
Yan-Sheng LU ◽  
Peng PAN ◽  
Liang HONG ◽  
Qiong WEI
Keyword(s):  
Moving Objects ◽  
Indexing Method

Use of Big Data to Evaluate and Improve Performance of Traffic Signal Systems in Resource-Constrained Countries

2017 ◽  
Vol 2620 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Yang Carl Lu ◽  
Holly Krambeck ◽  
Liang Tang
Keyword(s):  
Traffic Control ◽  
Time Of Day ◽  
Traffic Signal ◽  
Signal Timing ◽  
Gps Data ◽  
Resource Constrained ◽  
Physical Sensors ◽  
Constrained Environments ◽  
Traffic Signal Timing ◽  
High Level

Deployment of an adaptive area traffic control system is expensive; physical sensors require installation, calibration, and regular maintenance. Because of the high level of technical and financial resources required, area traffic control systems found in developing countries often are minimally functioning. In Cebu City, Philippines, for example, the Sydney Coordinated Adaptive Traffic System was installed before 2000, and fewer than 35% of detectors were still functioning as of January 2015. To address this challenge, a study was designed to determine whether taxi company GPS data are sufficient to evaluate and improve traffic signal timing plans in resource-constrained environments. If this work is successful, the number of physical sensors required to support those systems may be reduced and thereby substantially lower the costs of installation and maintenance. Taxi GPS data provided by a regional taxi-hailing app were used to design and implement methodologies for evaluating the performance of traffic signal timing plans and for deriving updated fixed-dynamic plans, which are fixed plans (with periods based on observable congestion patterns rather than only time of day) iterated regularly until optimization is reached. To date, three rounds of iterations have been conducted to ensure the stability of the proposed signal timings. Results of exploratory analysis indicate that the algorithm is capable of generating reasonable green time splits, but cycle length adjustment must be considered in the future.

On the Problem of Moving Objects With Autonomous Robots: A Unifying High-Level Planning Approach

2016 ◽  
Vol 1 (1) ◽  
pp. 469-476 ◽  
Author(s):  
Hamal Marino ◽  
Mirko Ferrati ◽  
Alessandro Settimi ◽  
Carlos Rosales ◽  
Marco Gabiccini
Keyword(s):  
Moving Objects ◽  
Autonomous Robots ◽  
Planning Approach ◽  
High Level

ASSIGNMENT QUERY AND ITS IMPLEMENTATION IN MOVING OBJECT DATABASES

2010 ◽  
Vol 09 (03) ◽  
pp. 349-372 ◽  
Author(s):  
ALİ R. KONAN ◽  
TAFLAN İ. GÜNDEM ◽  
MURAT E. KAYA
Keyword(s):  
Nearest Neighbor ◽  
Perfect Match ◽  
Moving Object ◽  
Location Based Services ◽  
Matching Problem ◽  
Total Cost ◽  
Wide Range ◽  
Weighted Bipartite Graph ◽  
Object Databases ◽  
Moving Object Databases

Moving object databases (MOD) are being used in a wide range of location-based services that are of growing interest in many application areas. In the literature, several query types such as nearest neighbor, reverse nearest neighbor, k-nearest neighbor, and proximity queries have been considered in MOD. In this paper, we propose a novel operator called the assignment operator as a query type for MOD. The assignment operator is an operator used in a query to solve the assignment problem (also known as the weighted bipartite graph-matching problem). Assignment operator finds a perfect match between two sets of objects in a manner that minimizes a total cost. For instance, a set of moving objects such as taxi cabs are assigned to a set of customers in a manner that minimizes the total cost of traveling for the taxis. A possible implementation of the assignment operator in MOD and its performance evaluation are given.

scholarly journals Discovery of Frequent Sequence Pattern in Moving Object Databases

2008 ◽  
Vol 15D (2) ◽  
pp. 179-186
Author(s):  
Thi Hong Nhan Vu ◽  
Bum-Ju Lee ◽  
Keun-Ho Ryu
Keyword(s):  
Moving Object ◽  
Sequence Pattern ◽  
Frequent Sequence ◽  
Object Databases ◽  
Moving Object Databases

scholarly journals Globular Gaussian Essential Part in Meager Bayesian Knowledge Structure for Nonlinear Deterioration

2021 ◽  
pp. 60-67
Author(s):  
Arun J ◽  
Gokulakrishnan V
Keyword(s):  
Line Segment ◽  
Sampling Method ◽  
Local Density ◽  
Knowledge Structure ◽  
Sampling Techniques ◽  
Trajectory Similarity ◽  
Object Databases ◽  
Similarity Information ◽  
Moving Object Databases ◽  
Trajectory Segmentation

Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

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