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Spatiotemporal Routing Analysis for Emergency Response in Indoor Space
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 Title & Authors
Spatiotemporal Routing Analysis for Emergency Response in Indoor Space
Lee, Jiyeong; Kwan, Mei-Po;
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 Abstract
Geospatial research on emergency response in multi-level micro-spatial environments (e.g., multi-story buildings) that aims at understanding and analyzing human movements at the micro level has increased considerably since 9/11. Past research has shown that reducing the time rescuers needed to reach a disaster site within a building (e.g., a particular room) can have a significant impact on evacuation and rescue outcomes in this kind of disaster situations. With the purpose developing emergency response systems that are capable of using complex real-time geospatial information to generate fast-changing scenarios, this study develops a Spatiotemporal Optimal Route Algorithm (SORA) for guiding rescuers to move quickly from various entrances of a building to the disaster site (room) within the building. It identifies the optimal route and building evacuation bottlenecks within the network in real-time emergency situations. It is integrated with a Ubiquitous Sensor Network (USN) based tracking system in order to monitor dynamic geospatial entities, including the dynamic capacities and flow rates of hallways per time period. Because of the limited scope of this study, the simulated data were used to implement the SORA and evaluate its effectiveness for performing 3D topological analysis. The study shows that capabilities to take into account detailed dynamic geospatial data about emergency situations, including changes in evacuation status over time, are essential for emergency response systems.
 Keywords
Indoor GIS;Spatiotemporal Optimal Route;Building Evacuation;Emergency Response;3D Network-Based Topological Data Model;
 Language
English
 Cited by
 References
1.
Armstrong, M.P. (2002), Geographic information technologies and their potentially erosive effects on personal privacy, Studies in the Social Sciences, Vol. 27, pp. 19-28.

2.
Azevedo, C.L., Cardoso, J.L., and Ben-Akiva, M. (2014), Vehicle tracking using the k-shortest paths algorithm and dual graphs, Transportation Research Procedia, Vol. 1, No.1, pp. 3-11. crossref(new window)

3.
Cahan, B. and Ball, M. (2002), GIS at ground zero: spatial technologies bolsters World Trade Center response and recovery, GEO World, Vol. 15, pp. 26-29.

4.
Chen, X., Kwan, M.P., Li, Q., and Chen, J. (2012), A model for evacuation risk assessment with consideration of preand post-disaster factors, Computers, Environment and Urban Systems, Vol. 36, No. 3, pp. 207-217. crossref(new window)

5.
Church, R.L. and Marston, J.R. (2003), Measuring accessibility for people with a disability, Geographycal Analysis, Vol. 35, No. 1, pp. 83-96. crossref(new window)

6.
Cova, T.J. and Church, R.L. (1997), Modeling community evacuation vulnerability using GIS, International Journal of Geographic Information Science, Vol. 11, pp. 763-784. crossref(new window)

7.
Cutter, S.L., Richardson, D.B., and Wilbanks T.J. (2003), The Geographical Dimensions of Terrorism, Routledge, New York, pp. 111-116.

8.
Dane, C. and Rizos, C. (1998), Positioning Systems in Intelligent Transportation Systems, Artech House, Boston.

9.
Delling, D., Goldberg, A.V., Nowatzyk, A., and Werneck, R.F. (2013), Phast : hardware-accelerated shortest path trees, Journal of Parallel and Distributed Computing, Vol. 73, No. 7, pp. 940-952. crossref(new window)

10.
Dijkstra, E.W. (1959), A note on two problems in connection with graphs, Numerische Mathematik, Vol. 1, pp. 269-271. crossref(new window)

11.
George, B., Kim, S., and Shekher, S. (2007), Spatio-temporal network databases and routing algorithms: a summary of results, In: Papadias, D., Zhang, D., and Kollios, G. (eds), SSTD 2007, LNCS, Vol. 4605, Springer, Verlag, pp. 460-477.

12.
Goodchild, M.F. (2003), Geospatial data in emergencies, In: Cutter, S.L., Richardson, D.B., and Wilbanks, T.J. (eds), The Geographical Dimensions of Terrorisms, Routledge, New York, pp. 99-104.

13.
Kirkby, S., Pollitt, S., and Eklund, P. (1997), Implementing a shortest path algorithm in a 3D GIS environment, In: Kraak, M.J. and Moleanaar, M. (eds), Advances in GIS Research II (Proceedings of the 7th International Symposium on Spatial Data Handling), Taylor & Francis, London, pp. 437-448.

14.
Kwan, M.P. (2003), Intelligent emergency response systems, In: Cutter, S.L., Richardson, D.B., and Wilbanks, T.J. (eds), The Geographical Dimensions of Terrorism, Routledge, New York, pp. 111-116.

15.
Kwan, M.P. and Lee, J. (2005), Emergency response after 9/11: the potential of real-time 3D GIS for quick emergency response in micro-spatial environments, Computers, Environment and Urban Systems, Vol. 29, pp. 93-113. crossref(new window)

16.
Kwan, M.P. and Ransberger, D. (2010), LiDAR assisted emergency response: detection of transport network obstructions caused by major disasters, Computers, Environment and Urban Systems, Vol. 34, pp. 179-188. crossref(new window)

17.
Kwan, M.P. and Weber, J. (2003), Individual accessibility revisited: implications for geographical analysis in the twenty-first century, Geographical Analysis, Vol. 35, No. 4, pp. 341-353. crossref(new window)

18.
Kolbe, T.H., Becker, T., and Nagel, C. (2008), 1st Technical Report Discussion of Euclidean Space and Cellular Space and Proposal of an Integrated Indoor Spatial Data Model, Technische Universitat Berlin.

19.
Lee, J. (2001), A 3D Data Model for Representing Topological Relationships between Spatial Entities in Built Environments, Ph.D. dissertation, Department of Geography, The Ohio State University, Columbus, Ohio, USA.

20.
Lee, J. (2004), A spatial access oriented implementation of a topological data model for 3D urban entities, GeoInformatica, Vol. 8, No. 3, pp. 235-262.

21.
Lee, J. (2007), A three-dimensional navigable data model to support emergency response in micro-spatial builtenvironments, Annals of the Association of American Geographers, Vol. 97, No.3, pp. 512-529. crossref(new window)

22.
Lim, Y. and Kim, H. (2005), A shortest path for real road network based on path overlap, Journal of Eastern Asia Society for Transportation Studies, Vol. 6, pp. 1426-1438.

23.
Meijers, M., Zlatanova, S., and Pfeifer, N. (2005), 3D geoinformation indoors: structuring for evacuation, Proceedings of Next Generation 3D City Models, 21-22 June, Bonn, Germany, http://www.eurosdr.net/km_pub/no49/html/Citymodel/paperpresentation.htm (last date accessed : 2 April 2010).

24.
Miller, H. and Shaw, S.L. (2001), Geographic Information System for Transportation: Principles and Applications, Oxford University Press, New York.

25.
Okabe, A. and Kitamura, M. (1996), A computational method for market area analysis on a network, Geographical Analysis, Vol. 28, pp. 330-349.

26.
Pu, S. and Zlatanova, S. (2005), Evacuation route calculation of inner buildings, In: Van Oosterom, P.J.M., Zlatanova, S., and Fendel, E.M. (eds), Geo-information for Disaster Management, Springer Verlag, Heidelberg, pp. 1143-1161.

27.
Raper, J. (2000), Meltidimensional Geographic Information Science, Taylor & Francis, New York.

28.
Scott, M.S. (1994), The development of an optimal path algorithm in three dimensional raster space, Proceedings of GIS/LIS'94, pp. 687-696.

29.
Xiong, D. (2000), A three-stage computational approach to network matching, Transportation Research C, Vol. 8, pp. 13-36. crossref(new window)

30.
Wu, Y. and Miller, H. (2002), Computational tools for measuring space-time accessibility within transportation networks with dynamic flow, Journal of Transportation and Statistics, Vol. 4, No. 2/3, pp. 1-14.

31.
Yen, J.Y. (1971), Finding the K shortest loopless paths in a network, Management Sciences, Vol. 17, No. 11, pp. 712-716. crossref(new window)

32.
Zhan, F.B. and Noon, C.E. (1998), Shortest paths algorithms: An evaluation using real road networks, Transportation Science, Vol. 32, pp. 65-73. crossref(new window)

33.
Zhang, S. (2013), One dynamic shortest path algorithm in a traffic network based on a genetic algorithm, Advances in Civil, Transportation and Environmental Engineering, Vol. 140, pp. 189-193.