Abstract
This contribution discusses an approach on finding significant locations from a set of GPS tracks, called Hotspots in this contribution. Based on that, a model where traffic infrastructure is represented by a dynamic network of Hotspots is suggested. Besides the location of Hotspots, information about travel times between these Hotspot-Nodes also comes along with the extracted significant places. This information can be used to improve or enrich traffic management and/or navigation systems by consequently achieving a more precise estimation of travel times compared to current systems.
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References
Rehrl, K., Leitinger, S., Krampe, S., Stumptner, R.: An Approach to Semantic Processing of GPS Traces. In: Laube,P., Klippel, A., Van den Weghe, N., Billen, R. (eds.) Proceedings of the 1st Workshop on Movement Pattern Analysis, MPA 2010, Zurich, pp. 136–142 (2010)
Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal Ubiquitous Comput 7(5), 275–286 (2003)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Ungar, L., Craven, M., Gunopulos, D., Eliassi-Rad, T. (eds.) Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, pp. 935–940 (2006)
Agamennoni, G., Nieto, J., Nebot, E.: Vehicle activity segmentation from position data. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 330–336. IEEE, Los Alamitos (2010)
Agamennoni, G., Nieto, J., Nebot, E.: Mining GPS data for extracting significant places. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Piscataway, pp.1860-1867 (2009)
Palma, A., Bogorny, V., Kuijpers, B., Alvares, L.: A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM Symposium on Applied Computing, New York, pp. 863–868 (2008)
Birant, D., Kut, A.: ST-DBSCAN: An algorithm for clustering spatial-temporal data. Data & Knowledge Engineering 60(1), 208–221 (2007)
Krampe, S.: Kleine Helden. ITS Magazine - Fachmagazin für Verkehrstechnik I(4) (2009)
Aiello, M.: Spatial reasoning: theory and practice. Institute for logic, Language and Computation (2002)
Renz, J., Nebel, B.: Spatial Reasoning with Topological Information. Springer, Freiburg (1998)
Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843 (1983)
Andrienko, N., Andrienko, G.: Exploratory analysis of spatial and temporal data: a systematic approach. Springer, Heidelberg (2006)
Skiadopoulos, S., Koubarakis, M.: Consistency checking for qualitative spatial reasoning with cardinal directions. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 341–356. Springer, Heidelberg (2002)
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Stumptner, R., Freudenthaler, B., Hönigl, J., Rehrl, K., Küng, J. (2012). Using GPS Trajectories to Create a Dynamic Network of Significant Locations as an Abstraction of Road Maps. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_21
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DOI: https://doi.org/10.1007/978-3-642-27549-4_21
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