Identifying Transportation Modes Using Gradient Boosting Decision Tree
Identifying the transportation modes could be applicable to many applications including personalized recommendation, transportation planning. The existing studies had not fully considered the impact of geographical information. In this paper, we propose a novel approach to detect transportation modes from massive trajectories using Gradient Boosting Decision Tree (GBDT), which adopted and estimated the impact of geographical information to achieve a better performance. In the experiments, we conduct the performance evaluation using the Geolife dataset which collected by 182 users over five years. The dataset contains 8347 trajectories with transportation mode such as driving, taking a bus, riding a bike and walking. 60% of trajectories are randomly chosen as training dataset, and then we tested on the remaining dataset. The experimental results showed that our proposed approach considering geographical information by using gradient boosting decision tree method achieve the precision of 84%, with the maximum increase of 6.83% to the traditional identifying transportation modes method. In addition, the geographical information contributed over 12% to improve the precision of recognition.
KeywordsTrajectories Transportation mode GBDT Pattern recognition Geographical features
This research was supported by the National Natural Science Foundation of China (Grant No. 41701521, 41771436), A Project of Shandong Province Higher Educational Science and Technology Program (Grant No. J15LH08) and Shandong Provincial Natural Science Foundation, China (Grant No. ZR2018LF005). We also thank the anonymous referees for their helpful comments and suggestions.
- 1.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, Seoul, Korea, 21–24 September 2008, pp. 312–321 (2008)Google Scholar
- 2.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, Beijing, China, 21–25 April 2008, pp. 247–256 (2008)Google Scholar
- 3.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, Chicago, IL, USA, 1–4 November 2011, pp. 54–63 (2011)Google Scholar
- 4.Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Using mobile phones to determine transportation modes. ACM Trans. Sens. Netw. (TOSN) 6, 13 (2010)Google Scholar
- 8.Widhalm, P., Nitsche, P., Brändie, N.: Transport mode detection with realistic smartphone sensor data. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, 11–15 November 2012, pp. 573–576 (2012)Google Scholar
- 9.Endo, Y., Toda, H., Nishida, K., Kawanobe, A.: Deep feature extraction from trajectories for transportation mode estimation. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, Ruili (eds.) PAKDD 2016. LNCS (LNAI), vol. 9652, pp. 54–66. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31750-2_5CrossRefGoogle Scholar