Browse > Article
http://dx.doi.org/10.7319/kogsis.2016.24.4.059

Synthetic Trajectory Generation Tool for Indoor Moving Objects  

Ryoo, Hyung Gyu (Department of Computer Engineering, Pusan National University)
Kim, Soo Jin (Department of Computer Engineering, Pusan National University)
Li, Ki Joune (Department of Computer Science and Engineering, Pusan National University)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.24, no.4, 2016 , pp. 59-66 More about this Journal
Abstract
For the performance experiments of databases systems with moving object databases, we need moving object trajectory data sets. For example, benchmark data sets of moving object trajectories are required for experiments on query processing of moving object databases. For those reasons, several tools have been developed for generating moving objects in Euclidean spaces or road network spaces. Indoor space differs from outdoor spaces in many aspects and moving object generator for indoor space should reflect these differences. Even some tools were developed to produce virtual moving object trajectories in indoor space, the movements generated by them are not realistic. In this paper, we present a moving object generation tool for indoor space. First, this tool generates trajectories for pedestrians in an indoor space. And it provides a parametric generation of trajectories considering not only speed, number of pedestrians, minimum distance between pedestrians but also type of spaces, time constraints, and type of pedestrians. We try to reflect the patterns of pedestrians in indoor space as realistic as possible. For the reason of interoperability, several geospatial standards are used in the development of the tool.
Keywords
Indoor Space; Moving Objects; Synthetic Data Generation; IndoorGML;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Afyouni, I., Cyril, R. and Claramunt, C., 2012, Spatial models for context-aware indoor navigation systems: A survey, Journal of Spatial Information Science, Vol. 1, No. 4, pp. 85-123.
2 Brinkhoff, T., 2002, A framework for generating network-based moving objects, GeoInformatica, Vol. 6, No. 2, pp. 153-180.   DOI
3 Huang, C., Jin, P., Wang, H., Wang, N., Wan, S. and Yue, L., 2013, IndoorSTG: a extensible tool to generate trajectory data for indoor moving objects, Proc. of IEEE 14th International Conference on Mobile Data Management, IEEE Computer Society, Milan, Italy, pp. 341-343
4 Jensen, C. S., Lu, H. and Yang, B., 2010, Indoor-a new data management frontier, IEEE Data Engineering Bulletin, Vol. 33, No. 2, pp. 12-17.
5 Lee, J., Li, K. J., Zlatanova, S., Kolbe, T., Nagel, C. and Becker, T., 2014, OGC IndoorGML, Open Geospatial Consortium, 14-005r4, USA.
6 Li, H., Lu, H., Chen, X., Chen, G., Chen, K. and Shou, L., 2016, VITA: A versatile toolkit for generating indoor mobility data for real-world buildings, Proc. of VLDB 2016 Conference, VLDB endowment, New Delhi, India, pp. 1453-1456.
7 Li, K. J., 2008, Indoor space: A new notion of space, Proc. of International Symposium on Web and Wireless GIS, W2GIS SC, Shanghai, China, pp. 1-3.
8 Mautz, R., 2009, Overview of current indoor positioning systems, Geodezija ir kartograja, Vol. 35, No. 1, pp. 18-22.   DOI
9 Pfoser, D. and Theodoridis, Y. 2003, Generating semantics based trajectories of moving objects, Computers, Environment and Urban Systems, Vol. 2, No. 3, pp. 243-263.
10 Zheng, Y., Xie, X. and Ma, W. Y., 2010, Geolife: A collaborative social networking service among user, location and trajectory, IEEE Data Engineering Bulletin, Vol. 33, No. 2, pp. 32-39.
11 Zlatanova, S., Liu, L., Sithole, G., Zhao, J. and Mortari, F., 2014, Space subdivision for indoor applications, Technical Report, GISt Report No. 66, Delft University of Technology, Netherlands, p. 48.