Analytics and data science applied to the trajectory outlier detection

• Autorzy: Lopez Alexis J. , Quintero Perfecto M. , Hernandez Ana K.

Identyfikatory

DOI

10.23743/acs-2020-09

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

spatial-temporal data; trajectory outlier detection; trajectory clustering

PL

dane przestrzenno-czasowe; wykrywanie wartości odstających trajektorii; grupowanie trajektorii

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2020
• Tom: Vol. 16, no 2
• Strony: 5--17
• Opis fizyczny: Bibliogr. 16 poz., fig., tab.

Twórcy

autor

Lopez Alexis J.

• Tecnológico Nacional de México, Instituto Tecnológico de Apizaco, 90300, Carretera Apizaco-Tzompantepec, Esquina Av., Instituto Tecnologico S/N, Apizaco, Tlaxcala, México

autor

Quintero Perfecto M.

• Tecnológico Nacional de México, Instituto Tecnológico de Apizaco, 90300, Carretera Apizaco-Tzompantepec, Esquina Av., Instituto Tecnologico S/N, Apizaco, Tlaxcala, México

autor

Hernandez Ana K.

• Tecnológico Nacional de México, Instituto Tecnológico de Apizaco, 90300, Carretera Apizaco-Tzompantepec, Esquina Av., Instituto Tecnologico S/N, Apizaco, Tlaxcala, México

Bibliografia

• [1] 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.
• [2] 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.
• [3] 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.
• [4] 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.
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• [7] 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
• [8] 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
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• [10] Liu, Z., Pi, D., & Jiang, J. (2013). Density-based trajectory outlier detection algorithm. Journal of Systems Engineering and Electronics, 24(2), 335–340.
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• [12] 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
• [13] 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.
• [14] 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.
• [15] Shaikh, S.A., & Kitagawa, H. (2014). Efficient distance-based outlier detection on uncertain datasets of Gaussian distribution. World Wide Web, 17(4), 511–538.
• [16] 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.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).