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Qualitative relations between moving objects in a network changing its topological relations. (English) Zbl 1167.90392
Summary: The Qualitative Trajectory Calculus on Networks (QTC\(_{\text N})\) defines qualitative relations between two continuously moving point objects (MPOs) moving along a network. As prevailing in other research, this network is presumed static in QTC\(_{\text N}\). Actually, in many cases, networks are dynamic entities. For example in a road network, the opening of a bridge can temporarily close the connection between two junctions; traffic jams and traffic lights increase the time needed to travel from \(A\) to \(B\). Therefore, it is interesting to examine what happens with qualitative relations between two continuously moving point objects if there are changes in the network. In this paper, we introduce QTC\(_{\text{DN}^{\prime }}\), being the Qualitative Trajectory Calculus on Changing Networks able to handle topological network changes. Potential applications of the calculus in transportation are highlighted, clearly illustrating the usefulness of the calculus.
Reviewer: Reviewer (Berlin)
90B10 Deterministic network models in operations research
Full Text: DOI
[1] Badaloni, S.; Giacomin, M., The algebra IA^fuz: a framework for qualitative fuzzy temporal reasoning, Artificial intelligence, 170, 10, 872-908, (2006) · Zbl 1131.68530
[2] B. Bennett, Logical Representations for Automated Reasoning about Spatial Relationships, Ph.D. Thesis, UK, University of Leeds, School of Computer Studies, 1997.
[3] Bogaert, P.; Van de Weghe, N.; De Maeyer, Ph., Description, definition and proof of a qualitative state change of moving objects along a road network, (), 239-248
[4] Bogaert, P.; Van de Weghe, N.; Cohn, A.G.; Witlox, F.; De Maeyer, Ph., The qualitative trajectory calculus on networks, (), 21-39
[5] Claramunt, C.; Thériault, M., Fuzzy semantics for direction relations between composite regions, Information sciences, 160, 1-4, 73-90, (2004)
[6] Clementini, E.; Di Felice, P.; Hernandez, D., Qualitative representation of positional information, Artificial intelligence, 95, 2, 317-356, (1997) · Zbl 0894.68143
[7] Cohn, A.G.; Renz, J., Qualitative spatial representation and reasoning, () · Zbl 0974.68206
[8] Duckham, M.; Lingham, J.; Mason, K.; Worboys, M., Qualitative reasoning about consistency in geographic information, Information sciences, 176, 6, 601-627, (2006)
[9] Dylla, F.; Wallgrun, J.O., Qualitative spatial reasoning with conceptual neighborhoods for agent control, Journal of intelligent & robotic systems, 48, 1, 55-78, (2007)
[10] Forbus, D., Qualitative process theory, Artificial intelligence, 24, 1-3, 85-165, (1984)
[11] Freksa, C., Temporal reasoning based on semi-intervals, Artificial intelligence, 54, 199-227, (1992)
[12] A. Galton, A qualitative approach to continuity, in: P. Amsili, M. Borillo, L. Vieu (Eds.), Proc. 5th Workshop on Time, Space and Movement (TSM), 1995, pp. 17-30.
[13] Galton, A., Towards a qualitative theory of movement, (), 377-396
[14] Galton, A.; Worboys, M., Processes and events in dynamic geo-networks, (), 45-59
[15] Gerevini, A., Incremental qualitative temporal reasoning: algorithms for the point algebra and the ORD-Horn class, Artificial intelligence, 166, 1-2, 37-80, (2005) · Zbl 1132.68730
[16] Güting, R.H.; de Almeida, V.T.; Ding, Z.M., Modeling and querying moving objects in networks, The international journal on very large data bases (VLDB), 15, 2, 165-190, (2006)
[17] Kim, K.; Lopez, M.A.; Leutenegger, S.; Li, K., A network-based indexing method for trajectories of moving objects, (), 344-353
[18] Lee, S.; Park, S.; Kim, W.; Lee, D., An efficient location encoding method for moving objects using hierarchical administrative district and road network, Information sciences, 177, 3, 832-843, (2007)
[19] Li, X.; Lin, H., Indexing network-constrained trajectories for connectivity-based queries, International journal of geographical information systems, 20, 3, 303-328, (2006)
[20] Liu, F.; Do, T.T.; Hua, K.A., Dynamic range query in spatial network environments, (), 254-265
[21] Monferrer, M.; Lobo, F., Qualitative velocity, (), 29-39 · Zbl 1028.68634
[22] Nedas, K.A.; Egenhofer, M.J.; Wilmsen, D., Metric details of topological line – line relations, International journal of geographical information science, 21, 1, 21-48, (2007)
[23] Pfoser, D.; Jensen, C.S., Trajectory indexing using movement constraints, Geoinformatica, 9, 2, 93-115, (2005)
[24] D. Randell, M. Witkowski, Tracking regions using conceptual neighbourhoods, in: R. Lopez de Mantras, L. Saitta (Eds.), Proc. 16th European Conference on Artificial Intelligence (ECAI), Workshop on Spatial and Temporal Reasoning, 2004, pp. 63-71.
[25] Rebolledo, M., Rough intervals—enhancing intervals for qualitative modeling of technical systems, Artificial intelligence, 170, 8-9, 667-685, (2006)
[26] Saygin, Y.; Ulusoy, O.; Yazici, A., Dealing with fuzziness in active mobile database systems, Information sciences, 120, 1-4, 23-44, (1999)
[27] N. Van de Weghe, Representing and Reasoning about Moving Objects: A Qualitative Approach, Ph.D. Thesis, Belgium, Ghent University, Faculty of Sciences, Department of Geography, 2004.
[28] Vieu, L., Spatial representation and reasoning in artificial intelligence, (), 5-41
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