Abstract
Trajectory point classification can be described as a supervised sequence labeling problem, in which a model is trained by labeling data to predict the category of unknown points and identify key events in the trajectory. Due to the difficulty of labeling trajectory point, a large amount of trajectory data is either unlabeled or labeled in an imbalanced way. To make matters worse, traditional trajectory point classification methods are generally constrained to utilize the statistical features of the labeled data and the semantic features as well as the large amount of unlabeled data have not been well studied yet. For this reason, the performance of traditional trajectory point classification methods is far from satisfactory. To solve this problem, we transfer existing language model knowledge to construct the semantic features and construct a trajectory point classification model by combining both the motion features and semantic features. The simulation results show that, compared with the traditional methods, our method has improved the accuracy of trajectory point classification by three and seven percentage points in the classification of circular and turning movements respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhao, Z.J., Ji, G.L.: Research progress of spatial-temporal tra-jectory classification. J. Geo-Inf. Sci. 19, 289–297 (2017)
Zheng, Y., Liu, L., Wang, L., Xie, X.: Learning transportation mode from raw gps data for geographic applications on the web. In: Proceedings of the 17th international conference on World Wide Web, pp. 247–256 (2008)
Jahangiri, A., Rakha, H.A.: Applying machine learning techniques to transportation mode recognition using mobile phone sensor data. J. IEEE Trans. Intell. Ttransp. Syst. 16, 2406–2417 (2015)
Shafique, M.A., Hato, E.A.: Comparison among various classification algorithms for travel mode detection using sensors’ data collected by smartphones. In: International Conference on Computers in Urban Planning and Urban Management, pp. 175–181 (2015)
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 (2008)
Dodge, S., Weibel, R., Forootan, E.: Revealing the Physics of Movement: Comparing the Similarity of Movement Characteristics of Different Types of Moving Objects, pp. 419–434. Computers, Environment and Urban Systems (2009)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. J. Adv. Neural Inf. Process. Syst. 26, 1–9 (2013)
Li, Y., Fei, T., Zhang, F.: A regionalization method for clustering and partitioning based on trajectories from NLP perspective. Int. J. Geogr. Inf. Sci. 33, 2385–2405 (2019)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XlNet: Generalized autoregressive pretraining for language understanding. J. Adv. Neural Inf. Process. Syst. 32, 1–18 (2019)
Chiang, C.H., Lee, H.Y.: Pre-training a language model without human language. arXiv preprint arXiv:2012.11995 (2020)
Balkić, Z., Šoštarić, D., Horvat, G.: GeoHash and UUID identifier for multi-agent sys-tems. In: KES International Symposium on Agent and Multi-agent Systems: Technologies and Applications, pp. 290–298 (2012)
Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. In: Advances in Neural Information Processing Systems, pp. 15908–15919 (2021)
Parmar, N., et al.: Image transformer. In International Conference on Machine Learning, pp. 4055–4064 (2018)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detec-tion. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Salim, J.O.: Fuzzy based PID controller for speed control of DC motor using LabVIEW. J. WSEAS Trans. Syst. Control. 10, 154–159 (2015)
McGrew, J.S., How, J.P., Williams, B., Roy, N.: Air-combat strategy using approximate dynamic programming. J. Guidance Control Dyn. 33, 1641–1654 (2010)
Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system: In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xu, J., Xu, X., Ruan, G. (2022). Combining Statistical and Semantic Features for Trajectory Point Classification. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1744. Springer, Singapore. https://doi.org/10.1007/978-981-19-9297-1_30
Download citation
DOI: https://doi.org/10.1007/978-981-19-9297-1_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9296-4
Online ISBN: 978-981-19-9297-1
eBook Packages: Computer ScienceComputer Science (R0)