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Spatio-temporal Networks: Modeling, Storing, and Querying Temporally-Detailed Roadmaps

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Space-Time Integration in Geography and GIScience

Abstract

Given spatio-temporal networks (e.g., roadmaps with traffic speed reported as a time-series in 5 min increments over a typical day for each road-segment) and operators (e.g., network snapshot, shortest path or path evaluation), a spatio-temporal network model provides a computer representation to facilitate reasoning, analysis and algorithm design for important societal applications. For example, next generation routing services are estimated to save consumers hundreds of billions of dollars in terms of time and fuel saved by 2020. Developing a model for spatio-temporal networks is challenging due to potentially conflicting requirements of expressiveness and model simplicity. Related work in Time Geography models spatio-temporal movement and relationships via dimension-based representations such as space-time prisms and space-time trajectories. These representations are not adequate for many STN use-cases, such as spatio-temporal routing queries. To address these limitations, we discuss a novel model called time-aggregated graph (TAG) that allows the properties of the network to be modeled as a time series. This model retains spatial network information while reducing the temporal replication needed in other models, thus resulting in a much more efficient model for several computational techniques for routing problems. In this chapter, we discuss spatio-temporal networks as represented by time-aggregated graphs at a conceptual, logical, and physical level. This chapter also focuses on shortest path algorithms for spatio-temporal networks. We develop the topics via case studies using TAGs in context of Lagrangian shortest-path queries and evacuation route planning.

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References

  • Abou-Rjeili, A., & Karypis, G. (2006). Multilevel algorithms for partitioning power-law graphs. In 20th international parallel and distributed processing symposium, 2006. IPDPS 2006 (p. 10). IEEE.

    Google Scholar 

  • Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network flows. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Airlines. http://www.delta.com/

  • Batty, M. (2005). Agents, cells, and cities: New representational models for simulating multiscale urban dynamics. Environment and Planning A, 37, 1373–1394.

    Article  Google Scholar 

  • Ben-Akiva, M. (2002). Development of a deployable real-time dynamic traffic assignment system: Dynamit and dynamit-p user’s guide. Technical Report. Cambridge: Massachusetts Institute of Technology.

    Google Scholar 

  • Bertsekas, D. P. (1987). Dynamic programming: Deterministic and stochastic models. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Burns, L. (1979). Transportation, temporal, and spatial components of accessibility. Lexington: Lexington Books.

    Google Scholar 

  • Chabini, I. (1998). Discrete dynamic shortest path problems in transportation applications: Complexity and algorithms with optimal run time. Transportation Research Record: Journal of the Transportation Research Board, 1645(-1), 170–175.

    Article  Google Scholar 

  • Chabini, I., & Lan, S. (2002). Adaptations of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks. IEEE Transactions on Intelligent Transportation Systems, 3(1), 60–74.

    Article  Google Scholar 

  • Christakos, G., Bogaert, P., & Serre, M. (2001). Temporal GIS: Advanced functions for field-based applications. New York: Springer.

    Book  Google Scholar 

  • Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to algorithms. Cambridge, MA: MIT Press.

    Google Scholar 

  • Dehne, F., Omran, M. T., & Sack, J.-R. (2009). Shortest paths in time-dependent FIFO networks using edge load forecasts. In Proceedings of the second international workshop on computational transportation science, IWCTS’09 (pp. 1–6). New York, NY: ACM.

    Google Scholar 

  • Delling, D. (2008). Time-dependent SHARC-routing. In Proceedings of the 16th annual European symposium on algorithms, ESA’08 (pp. 332–343). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Delling, D., & Wagner, D. (2007). Landmark-based routing in dynamic graphs. In Experimental algorithms (pp. 52–65). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Demiryurek, U., Banaei-Kashani, F., & Shahabi, C. (2010). A case for time-dependent shortest path computation in spatial networks. In Proc. of the ACM SIGSPATIAL Intl. Conf. on Advances in GIS, GIS’10 (pp. 474–477).

    Google Scholar 

  • Demiryurek, U., Banaei-Kashani, F., Shahabi, C., & Ranganathan, A. (2011). Online computation of fastest path in time-dependent spatial networks. In Advances in spatial and temporal databases. LNCS 6849 (pp. 92–111). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Deutsch, C. (2007). UPS embraces high-tech delivery methods. www.nytimes.com/2007/07/12/business/12ups.html

  • DiBiase, D., MacEachren, A., Krygier, J., & Reeves, C. (1992). Animation and the role of map design in scientific visualization. Cartography and Geographic Information Science, 19(4), 201–214.

    Article  Google Scholar 

  • Dijkstra, E. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.

    Article  Google Scholar 

  • Ding, B., Yu, J., & Qin, L. (2008). Finding time-dependent shortest paths over large graphs. In Proceedings of the 11th international conference on Extending database technology: Advances in database technology (pp. 205–216). New York: ACM.

    Google Scholar 

  • Dreyfus, S. (1969). An appraisal of some shortest path algorithms. Operations Research, 17, 395–412.

    Article  Google Scholar 

  • Egenhofer, M., & Golledge, R. (1998). Spatial and temporal reasoning in geographic information systems. New York: Oxford University Press.

    Google Scholar 

  • Ford, L., & Fulkerson, D. (1958). Constructing maximal dynamic flows from static flows. Operations Research, 6(3), 419–433.

    Article  Google Scholar 

  • Ford, L., & Fulkerson, D. (1962). Flows in networks. Princeton: Princeton University Press.

    Google Scholar 

  • Francis, R. L., & Chalmet, L. G. (1984). A negative exponential solution to an evacuation problem (Technical Report Research Report No. 84–86) Washington, DC: National Bureau Of Standards, Center for Fire Research.

    Google Scholar 

  • Frank, A. (2003). 2: Ontology for spatio-temporal databases. Lecture Notes in Computer Science, 2520, 9.

    Article  Google Scholar 

  • Frank, A., Grumbach, S., Güting, R., Jensen, C., Koubarakis, M., Lorentzos, N., Manolopoulos, Y., Nardelli, E., Pernici, B., Schek, H., et al. (1999). Chorochronos: A research network for spatiotemporal database systems. ACM SIGMOD Record, 28(3), 12–21.

    Article  Google Scholar 

  • Galton, A., & Worboys, M. (2005). Processes and events in dynamic geo-networks. Lecture Notes in Computer Science, 3799, 45.

    Article  Google Scholar 

  • George, B. & Shekhar, S. (2006). Time-aggregated graphs for modeling spatio-temporal networks. In Advances in conceptual modeling – Theory and practice (pp. 85–99). Berlin/Heidelberg: Springer.

    Google Scholar 

  • George, B., & Shekhar, S. (2007a, November). Modeling spatio-temporal network computations – A summary of results. In Proceedings of second international conference on GeoSpatial Semantics (GeoS 2007), Mexico City, Mexico.

    Google Scholar 

  • George, B., & Shekhar, S. (2007b). Time-aggregated graphs for modeling spatio-temporal networks – An extended abstract. Journal on Data Semantics, XI. 191–212. Berlin/Heidelberg: Springer.

    Google Scholar 

  • George, B., Kang, J., & Shekhar, S. (2007a, August). Spatio-temporal sensor graphs: A data model for discovery of patterns in sensor data. In Proceedings of first international workshop on knowledge discovery in Sensor Data (SensorKDD) in connection with KDD’07.

    Google Scholar 

  • George, B., Kim, S., & Shekhar, S. (2007b, July). Spatio-temporal network databases and routing algorithms: A summary of results. In Proceedings of international symposium on Spatial and Temporal Databases (SSTD’07), Boston, MA.

    Google Scholar 

  • George, B., Shekhar, S., & Kim, S. (2008). Spatio-temporal network databases and routing algorithms (Technical Report 08–039). Minneapolis: University of Minnesota – Computer Science and Engineering.

    Google Scholar 

  • Goodchild, M., Yuan, M., & Cova, T. (2007). Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science, 21(3), 239–260.

    Article  Google Scholar 

  • Google Maps. http://maps.google.com

  • Gunturi, V., Shekhar, S., & Bhattacharya, A. (2010). Minimum spanning tree on spatio-temporal networks. In Proceedings of the 21st international conference on database and expert systems applications: Part II, DEXA’10 (pp. 149–158). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Gunturi, V. M. V., Nunes, E., Yang, K., & Shekhar, S. (2011). A critical-time-point approach to all-start-time Lagrangian shortest paths: A summary of results. Advances in spatial and temporal databases. LNCS 6849 (pp. 74–91). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. In Proceedings of the 1984 ACM SIGMOD international conference on management of data (pp. 47–57). New York: ACM.

    Google Scholar 

  • Hägerstrand, T. (1970). What about people in regional science? Papers in Regional Science, 24(1), 6–21.

    Article  Google Scholar 

  • Hamacher, H., & Tjandra, S. (2002). Mathematical modeling of evacuation problems: A state of the art. In Pedestrian and evacuation dynamics (pp. 227–266). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Harrower, M. (2004). A look at the history and future of animated maps. Cartographica: The International Journal for Geographic Information and Geovisualization, 39(3), 33–42.

    Article  Google Scholar 

  • Herrera, J., & Bayen, A. (2009). Incorporation of Lagrangian measurements in freeway traffic state estimation. Transportation Research Part B: Methodological, 44, 460–481.

    Article  Google Scholar 

  • Hoppe, B., & Tardos, E. (1994). Polynomial time algorithms for some evacuation problems. In Proceedings of the fifth annual ACM-SIAM symposium on discrete algorithms, SODA’94 (pp. 433–441). Society for Industrial and Applied Mathematics. Philadelphia, PA.

    Google Scholar 

  • Janelle, D. (2004). Impact of information technologies. In The geography of urban transportation (p. 86). New York, NY: Addison-Wesley.

    Google Scholar 

  • Joel Lovell (December 9, 2007). Left-hand-turn elimination. New York Times. http://goo.gl/3bkPb

  • Kanoulas, E., Du, Y., Xia, T., & Zhang, D. (2006). Finding fastest paths on a road network with speed patterns. In Proceedings of the 22nd International Conference on Data Engineering (ICDE) (p. 10). IEEE.

    Google Scholar 

  • Kaufman, D., & Smith, R. (1993). Fastest paths in time-dependent networks for intelligent vehicle highway systems applications. IVHS Journal, 1(1), 1–11.

    Google Scholar 

  • Kim, S., George, B., & Shekhar, S. (2007). Evacuation route planning: Scalable heuristics. In Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems (ACM-GIS) (pp. 1–8). New York, NY: ACM.

    Google Scholar 

  • Kisko, T. M., & Francis, R. L. (1985). Evacnet+: A computer program to determine optimal building evacuation plans. Fire Safety Journal, 9(2), 211–220.

    Article  Google Scholar 

  • Kisko, T., Francis, R., & Nobel, C. (1998). EVACNET4 user’s guide. University of Florida.

    Google Scholar 

  • Kohler, E., Langtau, K., & Skutella, M. (2002). Time-expanded graphs for flow-dependent transit times. In Proceedings 10th annual European symposium on algorithms (pp. 599–611). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Kriegel, H.-P., Renz, M., & Schubert, M. (2010). Route skyline queries: A multi-preference path planning approach. In 26th international conference on data engineering (ICDE), 2010 IEEE (pp. 261–272). IEEE. Long Beach, CA.

    Google Scholar 

  • Kwan, M. (2004). GIS methods in time-geographic research: Geocomputation and geovisualization of human activity patterns. Geografiska Annaler: Series B, Human Geography, 86(4), 267–280.

    Article  Google Scholar 

  • Langran, G. (1992). Time in geographic information systems. London: Taylor & Francis.

    Google Scholar 

  • Langran, G., & Chrisman, N. (1988). A framework for temporal geographic information. Cartographica: The International Journal for Geographic Information and Geovisualization, 25(3), 1–14.

    Article  Google Scholar 

  • Lenntorp, B. (1977). Paths in space-time environments: A time-geographic study of movement possibilities of individuals. Environment and Planning A, 9, 961–972.

    Article  Google Scholar 

  • Lu, Q., Huang, Y., & Shekhar, S. (2003). Evacuation planning: A capacity constrained routing approach. In First NSF/NIJ symposium on intelligence and security informatics (pp. 111–125). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Lu, Q., George, B., & Shekhar, S. (2005). Capacity constrained routing algorithms for evacuation planning: A summary of results. In Proceedings of 9th International Symposium on Spatial and Temporal Databases (SSTD™05) (pp. 22–24). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Lu, Q., George, B., & Shekhar, S. (2007). Evacuation route planning: A case study in semantic computing. International Journal of Semantic Computing, 1(2), 249. World Scientific Publishing.

    Article  Google Scholar 

  • Luenberger, D. (1973). Introduction to linear and nonlinear programming. Reading: Addison-Wesley.

    Google Scholar 

  • Mahmassani, H. S., Sbayti, H., & Zhou, X. (2004). Dynasmart-p version 1.0 user’s guide. (Technical Report). Maryland Transportation Initiative, University of Maryland.

    Google Scholar 

  • Mapquest. http://www.mapquest.com/

  • Maps. http://www.bing.com/maps/

  • McIntosh, J., & Yuan, M. (2005). Assessing similarity of geographic processes and events. Transactions in GIS, 9(2), 223–245.

    Article  Google Scholar 

  • Miller, H. (1999). Measuring space-time accessibility benefits within transportation networks: Basic theory and computational procedures. Geographical Analysis, 31(2), 187–212.

    Article  Google Scholar 

  • Miller, H. (2005). A measurement theory for time geography. Geographical Analysis, 37(1), 17–45.

    Article  Google Scholar 

  • Monmonier, M. (1990). Strategies for the visualization of geographic time-series data. Cartographica: The International Journal for Geographic Information and Geovisualization, 27(1), 30–45.

    Article  Google Scholar 

  • O’Sullivan, D. (2005). Geographical information science: Time changes everything. Progress in Human Geography, 29(6), 749.

    Article  Google Scholar 

  • Orda, A., & Rom, R. (1990). Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length. Journal of the ACM (JACM), 37(3), 607–625.

    Article  Google Scholar 

  • Orda, A., & Rom, R. (1991). Minimum weight paths in time-dependent networks. Networks, 21, 295–319.

    Article  Google Scholar 

  • Pallottino, S., & Scuttella, M. G. (1998). Shortest path algorithms in transportation models: Classical and innovative aspects. In Equilibrium and advanced transportation modelling (pp. 245–281). Kluwer. Springer US.

    Google Scholar 

  • Peuquet, D., & Duan, N. (1995). An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data. International Journal of Geographical Information Science, 9(1), 7–24.

    Article  Google Scholar 

  • Pred, A. (1977). The choreography of existence: Comments on Hagerstrand’s time-geography and its usefulness. Economic Geography, 53, 207–221.

    Article  Google Scholar 

  • Russel, S., & Norwig, P. (1995). Artificial intelligence: A modern approach. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Samet, H. (1995). Spatial data structures. In Modern database systems, the object model, interoperability and beyond (pp. 361–385). New York, NY: Addison-Wesley.

    Google Scholar 

  • Shekhar, S., & Liu, D.-R. (1997). CCAM: A connectivity-clustered access method for networks and network computations. IEEE Transactions on Knowledge and Data Engineering, 9(1), 102–119.

    Article  Google Scholar 

  • Shekhar, S., & Xiong, H. (2007). Encyclopedia of GIS. Berlin: Springer.

    Google Scholar 

  • Timmermans, H., Arentze, T., & Joh, C. (2002). Analysing space-time behaviour: New approaches to old problems. Progress in Human Geography, 26(2), 175.

    Article  Google Scholar 

  • Torrens, P. (2006). Simulating sprawl. Annals of the Association of American Geographers, 96(2), 248–275.

    Article  Google Scholar 

  • USA-Today. (2011, June). Evacuation plans age as population near nuclear plants soars.

    Google Scholar 

  • Vasconez, K., & Kehrli, M. (2010). Highway evacuations in selected metropolitan areas: Assessment of impediments (Technical Report FHWA-HOP-10-059). Washington, DC: Federal Highway Administration, Office of Transportation Operations.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Minnesota, 4-192 Keller Hall 200 Union Street SE, Minneapolis, MN, 55455, USA

    Michael R. Evans, KwangSoo Yang, Viswanath Gunturi, Betsy George & Shashi Shekhar

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  1. Michael R. Evans
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  2. KwangSoo Yang
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  3. Viswanath Gunturi
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  4. Betsy George
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  5. Shashi Shekhar
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Correspondence to Michael R. Evans .

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Editors and Affiliations

  1. Department of Geography and Geographic Information Science, University of Illinois, Urbana-Champaign, Illinois, USA

    Mei-Po Kwan

  2. Association of American Geographers, Washington, District of Columbia, USA

    Douglas Richardson

  3. Department of Geography, Hong Kong Baptist University, Hong Kong, China

    Donggen Wang

  4. Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences, Beijing, China

    Chenghu Zhou

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Evans, M.R., Yang, K., Gunturi, V., George, B., Shekhar, S. (2015). Spatio-temporal Networks: Modeling, Storing, and Querying Temporally-Detailed Roadmaps. In: Kwan, MP., Richardson, D., Wang, D., Zhou, C. (eds) Space-Time Integration in Geography and GIScience. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9205-9_6

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