Generating network-based moving objects

被引:40
|
作者
Brinkhoff, T [1 ]
机构
[1] Univ APpl Sci, Fachhsch Oldenburg Ostfriesland Wilhelmshaven, IAPG, D-26121 Oldenburg, Germany
关键词
D O I
10.1109/SSDM.2000.869794
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Benchmarking spatiotemporal database systems requires the generation of suitable datasets simulating the typical behavior of moving objects. Previous approaches do not consider that in many applications the moving objects follow a given network. In this paper, the most important properties of network-based moving objects are presented. These properties are the basis for specifying and developing a new generator for spatiotemporal data. This generator combines a real network with user-defined properties of the resulting dataset. A framework for using and promoting the generator exists.
引用
收藏
页码:253 / 255
页数:3
相关论文
共 50 条
  • [21] Neural network based method for background modeling and detecting moving objects
    Bi Song
    Han Cunwu
    Sun Dehui
    The Journal of China Universities of Posts and Telecommunications, 2015, 22 (03) : 100 - 109
  • [22] Shape detection of moving objects based on a neural network of a light line
    Muñoz-Rodríguez, JA
    Asundi, A
    Rodríguez-Vera, R
    OPTICS COMMUNICATIONS, 2003, 221 (1-3) : 73 - 86
  • [23] Techniques for efficient road-network-based tracking of moving objects
    Civilis, A
    Jensen, CS
    Pakalnis, S
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2005, 17 (05) : 698 - 712
  • [24] A Grid Based Trajectory Indexing Method for Moving Objects on Fixed Network
    Huang, Menglong
    Hu, Peng
    Xia, Lanfang
    2010 18TH INTERNATIONAL CONFERENCE ON GEOINFORMATICS, 2010,
  • [25] A BP neural network-based stage identification method for moving loads on bridges
    Li, Zhong-Xian
    Chen, Feng
    Wang, Bo
    Gongcheng Lixue/Engineering Mechanics, 2008, 25 (09): : 85 - 92
  • [26] A safe-exit approach for efficient network-based moving range queries
    Yung, Duncan
    Yiu, Man Lung
    Lo, Eric
    DATA & KNOWLEDGE ENGINEERING, 2012, 72 : 126 - 147
  • [27] Neural Network-Based Nonlinear System Identification for Generating Stochastic Models with Distribution Estimation
    Yamada, Keito
    Maruta, Ichiro
    Fujimoto, Kenji
    2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, 2023, : 5500 - 5505
  • [28] An Open-Source Framework of Generating Network-Based Transit Catchment Areas by Walking
    Lin, Diao
    Zhu, Ruoxin
    Yang, Jian
    Meng, Liqiu
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2020, 9 (08)
  • [29] Convolutional Neural Network-Based Electromagnetic Imaging of Uniaxial Objects in a Half-Space
    Chiu, Chien-Ching
    Chiang, Jen-Shiun
    Chen, Po-Hsiang
    Jiang, Hao
    SENSORS, 2025, 25 (06)
  • [30] Tracking Fast Moving Objects by Segmentation Network
    Zita, Ales
    Sroubek, Filip
    2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2021, : 10312 - 10319