ANALYTICS AND DATA SCIENCE APPLIED TO THE TRAJECTORY OUTLIER DETECTION
Alexis J. LOPEZ
alexistegno@gmail.comTecnológico Nacional de México, Instituto Tecnológico de Apizaco, 90300, Carretera Apizaco-Tzompantepec, Esquina Av., Instituto Tecnologico S/N, Apizaco, Tlaxcala (Mexico)
Perfecto M. QUINTERO
Tecnológico Nacional de México, Instituto Tecnológico de Apizaco, 90300, Carretera Apizaco-Tzompantepec, Esquina Av., Instituto Tecnologico S/N, Apizaco, Tlaxcala (Mexico)
Ana K. HERNANDEZ
Tecnológico Nacional de México, Instituto Tecnológico de Apizaco, 90300, Carretera Apizaco-Tzompantepec, Esquina Av., Instituto Tecnologico S/N, Apizaco, Tlaxcala (Mexico)
Abstract
Nowadays, logistics for transportation and distribution of merchandise are a key element to increase the competitiveness of companies. However, the election of alternative routes outside the panned routes causes the logistic companies to provide a poor-quality service, with units that endanger the appropriate deliver of merchandise and impacting negatively the way in which the supply chain works. This paper aims to develop a module that allows the processing, analysis and deployment of satellite information oriented to the pattern analysis, to find anomalies in the paths of the operators by implementing the algorithm TODS, to be able to help in the decision making. The experimental results show that the algorithm detects optimally the abnormal routes using historical data as a base.
Keywords:
spatial-temporal data, trajectory outlier detection, trajectory clusteringReferences
Cao, K., Shi, L., Wang, G., Han, D., & Bai, M. (2014). Density-Based Local Outlier Detection on Uncertain Data. In: F. Li, G. Li, S.W. Hwang, B. Yao & Z. Zhang, (Eds.), Web-Age Information Management (pp. 67–71). Springer International Publishing, Cham.
DOI: https://doi.org/10.1007/978-3-319-08010-9_9
Google Scholar
Domínguez, D.R., Redondo, R.P.D., Vilas, A.F., & Khalifa, M.B. (2017). Sensing the city with Instagram: Clustering geolocated data for outlier detection. Expert Systems with Applications, 78, 319–333.
DOI: https://doi.org/10.1016/j.eswa.2017.02.018
Google Scholar
Fontes, V.C., de Alencar, L.A., Renso, C., & Bogorny, V. (2013). Discovering Trajectory Outliers between Regions of Interest. In Proceedings of XIV GEOINFO (p. 12). Campos do Jordao, Brazil.
Google Scholar
Gan, J., & Tao, Y. (2015). DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data – SIGMOD ’15 (pp. 519–530). ACM Press, Melbourne, Victoria, Australia.
DOI: https://doi.org/10.1145/2723372.2737792
Google Scholar
Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques. Third edition. Elsevier.
Google Scholar
Hazel, G.G. (2008). Multivariate Gaussian MRF for multispectral scene segmentation and anomaly detection. In IEEE Transactions on Geoscience and Remote Sensing, 38(3), 1199–1211.
DOI: https://doi.org/10.1109/36.843012
Google Scholar
Lee, J.G., Han, J., & Li, X. (2008). Trajectory Outlier Detection: A Partition-and-Detect Framework. In: 2008 IEEE 24th International Conference on Data Engineering (pp. 140–149). https://doi.org/10.1109/ICDE.2008.4497422
DOI: https://doi.org/10.1109/ICDE.2008.4497422
Google Scholar
Lei, B., & Mingchao, D. (2018). A distance-based trajectory outlier detection method on maritime traffic data. In 2018 4th International Conference on Control, Automation and Robotics (ICCAR) (pp. 340–343). https://doi.org/10.1109/ICCAR.2018.8384697
DOI: https://doi.org/10.1109/ICCAR.2018.8384697
Google Scholar
Liao, T.W. (2005). Clustering of time series data—a survey. Pattern Recognition, 38(11), 1857–1874.
DOI: https://doi.org/10.1016/j.patcog.2005.01.025
Google Scholar
Liu, Z., Pi, D., & Jiang, J. (2013). Density-based trajectory outlier detection algorithm. Journal of Systems Engineering and Electronics, 24(2), 335–340.
DOI: https://doi.org/10.1109/JSEE.2013.00042
Google Scholar
Markovic, N., Sekula, P., Vander Laan, Z., Andrienko, G., & Andrienko, N. (2019). Applications of Trajectory Data From the Perspective of a Road Transportation Agency: Literature Review and Maryland Case Study. IEEE Transactions on Intelligent Transportation Systems, 20(5), 1858–1869. https://doi.org/10.1109/TITS.2018.2843298
DOI: https://doi.org/10.1109/TITS.2018.2843298
Google Scholar
Munoz-Organero, M., Ruiz-Blaquez, R., & Sánchez-Fernández, L. (2018). Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving. Computers, Environment and Urban Systems, 68, 1–8. https://doi.org/10.1016/j.compenvurbsys.2017.09.005
DOI: https://doi.org/10.1016/j.compenvurbsys.2017.09.005
Google Scholar
Sarmento, J., Renneboog, L., & Matos, P.V. (2017). Measuring highway efficiency by a DEA approach and the Malmquist index. European Journal of Transport and Infrastructure Research, 17(4), 530–551.
Google Scholar
Schmitt, J.P., & Baldo, F. (2018). A Method to Suggest Alternative Routes Based on Analysis of Automobiles’ Trajectories. In: 2018 XLIV Latin American Computer Conference (CLEI) (pp. 436–444). http://doi.org/10.1109/CLEI.2018.00059.
DOI: https://doi.org/10.1109/CLEI.2018.00059
Google Scholar
Shaikh, S.A., & Kitagawa, H. (2014). Efficient distance-based outlier detection on uncertain datasets of Gaussian distribution. World Wide Web, 17(4), 511–538.
DOI: https://doi.org/10.1007/s11280-013-0211-y
Google Scholar
Yuan, G., Sun, P., Zhao, J., Li, D., & Wang, C. (2017). A review of moving object trajectory clustering algorithms. Artificial Intelligence Review, 47(1), 123–144.
DOI: https://doi.org/10.1007/s10462-016-9477-7
Google Scholar
Authors
Alexis J. LOPEZalexistegno@gmail.com
Tecnológico Nacional de México, Instituto Tecnológico de Apizaco, 90300, Carretera Apizaco-Tzompantepec, Esquina Av., Instituto Tecnologico S/N, Apizaco, Tlaxcala Mexico
Authors
Perfecto M. QUINTEROTecnológico Nacional de México, Instituto Tecnológico de Apizaco, 90300, Carretera Apizaco-Tzompantepec, Esquina Av., Instituto Tecnologico S/N, Apizaco, Tlaxcala Mexico
Authors
Ana K. HERNANDEZTecnológico Nacional de México, Instituto Tecnológico de Apizaco, 90300, Carretera Apizaco-Tzompantepec, Esquina Av., Instituto Tecnologico S/N, Apizaco, Tlaxcala Mexico
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