Abstract
Systems for traffic events administration are important tools in the prediction of disasters and management of that of the movement flow in diverse contexts. These systems are generally developed on non-fuzzy grouping algorithms and ontologies. However, the results of the implementation do not always give high precision scores due to different factors such as data heterogeneity, the high number of components used in their architecture and to the mixture of highly specialized and diverse domain ontologies. These factors do not ease the implementation of the systems able to predict with higher reliability traffic events. In this work, we design a system for traffic events detection that implements a new ontology called trafficstore and leverages the fuzzy c-means algorithm. The indexes evaluated on the fuzzy c-means algorithm demonstrates that the implemented system improves its efficiency in the grouping of traffic events.
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Notes
- 1.
The online documentation is available at http://linkedvocabs.org/onto/trafficstore/trafficstore.html.
- 2.
See the ontology online at https://linkedvocabs.org/onto/trafficstore/trafficstore.html.
References
Battle, R., Kolas, D.: Enabling the geospatial semantic web with parliament and geosparql. Semantic Web 3(4), 355–370 (2012)
Bermudez-Edo, M., Elsaleh, T., Barnaghi, P., Taylor, K.: IoT-Lite: a lightweight semantic model for the internet of things and its use with dynamic semantics. Pers. Ubiquitous Comput. 21(3), 475–487 (2017)
Bezdek, J.C.: Cluster validity with fuzzy sets (1973)
Bezdek, J.C.: Numerical taxonomy with fuzzy sets. J. Math. Biol. 1(1), 57–71 (1974)
Dk, O.D.: What is open data dk, March 2015. https://www.opendata.dk/hvad-er-open-data-dk
Elsaleh, T., Enshaeifar, S., Rezvani, R., Acton, S.T., Janeiko, V., Bermudez-Edo, M.: IoT-Stream: a lightweight ontology for internet of things data streams and its use with data analytics and event detection services. Sensors (Basel, Switz.) 20(4) (2020). https://doi.org/10.3390/s20040953
Gao, F., Ali, M.I., Mileo, A.: Semantic discovery and integration of urban data streams. Challenge 7, 16 (2014)
Gómez, S.A., Fillottrani, P.R.: Completitud de los métodos de acceso a datos basado en ontologías: enfoques, propiedades y herramientas. In: XIX Workshop de Investigadores en Ciencias de la Computación (2017)
Gorender, S., Silva, Í.: An ontology for a fault tolerant traffic information system. In: 22nd International Congress of Mechanical Engineering (COBEM 2013) (2013)
Janowicz, K., Haller, A., Cox, S., Phuoc, D., Lefranois, M.: SOSA: a lightweight ontology for sensors, observations, samples, and actuators. J. Web Semant. 56, 1–10 (2018). https://doi.org/10.1016/j.websem.2018.06.003
Kharlamov, E., et al.: Towards analytics aware ontology based access to static and streaming data. In: Groth, P. (ed.) ISWC 2016. LNCS, vol. 9982, pp. 344–362. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_31
Kim, D.W., Lee, K.H., Lee, D.: On cluster validity index for estimation of the optimal number of fuzzy clusters. Pattern Recogn. 37(10), 2009–2025 (2004)
London, I.C.: London air quality network (2021). https://www.londonair.org.uk/LondonAir/General/about.aspx
Morignot, P., Nashashibi, F.: An ontology-based approach to relax traffic regulation for autonomous vehicle assistance. arXiv preprint arXiv:1212.0768 (2012)
Nikolaou, C., Kostylev, E.V., Konstantinidis, G., Kaminski, M., Grau, B.C., Horrocks, I.: The bag semantics of ontology-based data access. arXiv preprint arXiv:1705.07105 (2017)
Rezvani, R., Enshaeifar, S., Barnaghi, P.: Lagrangian-based pattern extraction for edge computing in the Internet of Things. In: 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), pp. 177–182. IEEE (2019)
Tambassi, T.: From a geographical perspective: spatial turn, taxonomies and geo-ontologies. In: The Philosophy of Geo-Ontologies. SG, pp. 27–36. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-64033-4_3
Wu, K.L., Yang, M.S.: A cluster validity index for fuzzy clustering. Pattern Recogn. Lett. 26(9), 1275–1291 (2005)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841–847 (1991)
Acknowledgments
GA acknowledges grant ANR-19-CE23-0012 from Agence Nationale de la Recherche for project CoSWoT. We thank three anonymous reviewers for their helpful comments.
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Pérez, H.E., Mederos, A.L., Lio, D.G., Hurtado, L.E., Duarte, D.G., Atemezing, G.A. (2021). A System for Traffic Events Detection Using Fuzzy C-Means. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_6
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