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2018 | OriginalPaper | Chapter

Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal

Authors : Mohammad Etemad, Amílcar Soares Júnior, Stan Matwin

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features generation; (iii) trajectory features extraction; (iv) noise removal; (v) normalization. We show that the extraction of the new point features: bearing rate, the rate of rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores. We also show that the noise removal task affects the performance of all the models tested. Finally, the empirical tests where we compare this work against state-of-art transportation mode prediction strategies show that our framework is competitive and outperforms most of them.

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Literature
1.
go back to reference Dabiri, S., Heaslip, K.: Inferring transportation modes from GPS trajectories using a convolutional neural network. Transp. Res. Part C Emerg. Technol. 86, 360–371 (2018)CrossRef Dabiri, S., Heaslip, K.: Inferring transportation modes from GPS trajectories using a convolutional neural network. Transp. Res. Part C Emerg. Technol. 86, 360–371 (2018)CrossRef
2.
3.
go back to reference Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)MATH Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)MATH
4.
go back to reference Jiang, X., Souza, E.N., Pesaranghader, A., Hu, B., Silver, D.L., Matwin, S.: trajectorynet: an embedded GPS trajectory representation for point-based classification using recurrent neural networks. arXiv preprint arXiv:1705.02636 (2017) Jiang, X., Souza, E.N., Pesaranghader, A., Hu, B., Silver, D.L., Matwin, S.: trajectorynet: an embedded GPS trajectory representation for point-based classification using recurrent neural networks. arXiv preprint arXiv:​1705.​02636 (2017)
5.
go back to reference Soares Júnior, A., Moreno, B.N., Times, V.C., Matwin, S., dos Anjos Formiga Cabral, L.: GRASP-UTS: an algorithm for unsupervised trajectory segmentation. Int. J. Geogr. Inf. Sci. 29(1), 46–68 (2015)CrossRef Soares Júnior, A., Moreno, B.N., Times, V.C., Matwin, S., dos Anjos Formiga Cabral, L.: GRASP-UTS: an algorithm for unsupervised trajectory segmentation. Int. J. Geogr. Inf. Sci. 29(1), 46–68 (2015)CrossRef
6.
go back to reference Lin, M., Hsu, W.-J.: Mining GPS data for mobility patterns: a survey. Pervasive Mob. Comput. 12, 1–16 (2014)CrossRef Lin, M., Hsu, W.-J.: Mining GPS data for mobility patterns: a survey. Pervasive Mob. Comput. 12, 1–16 (2014)CrossRef
7.
go back to reference Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, J.A., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008)CrossRef Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, J.A., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008)CrossRef
8.
go back to reference Stenneth, L., Wolfson, O., Yu, P.S., Xu, B.: Transportation mode detection using mobile phones and GIS information. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2011, pp. 54–63. ACM, New York (2011) Stenneth, L., Wolfson, O., Yu, P.S., Xu, B.: Transportation mode detection using mobile phones and GIS information. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2011, pp. 54–63. ACM, New York (2011)
9.
go back to reference Xiao, Z., Wang, Y., Fu, K., Fan, W.: Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS 6(2), 57 (2017) Xiao, Z., Wang, Y., Fu, K., Fan, W.: Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS 6(2), 57 (2017)
10.
go back to reference Yanyun, G., Fang, Z., Shaomeng, C., Haiyong, L.: A convolutional neural networks based transportation mode identification algorithm. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7, September 2017 Yanyun, G., Fang, Z., Shaomeng, C., Haiyong, L.: A convolutional neural networks based transportation mode identification algorithm. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7, September 2017
11.
go back to reference Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.-Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 312–321. ACM (2008) Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.-Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 312–321. ACM (2008)
Metadata
Title
Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal
Authors
Mohammad Etemad
Amílcar Soares Júnior
Stan Matwin
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-319-89656-4_24

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