Abstract
Understanding meaningful information such as transportation mode (e.g., walking, bus) from Global Positioning System (GPS) data has great advantages in urban management and environmental protection. However, the urban traffic environment has evolved from “data poor” to “data rich”, resulting in the decline in the accuracy of transportation mode detection results. In this paper, an enhanced approach for effectively detecting transportation mode with a detection model and correction method from GPS data is proposed. Specifically, we make the following contributions. First, a trajectory segmentation method is proposed to detect single-mode segments. Secondly, a Random Forest (RF)-based detection model containing several new features is introduced to enhance discrimination. Finally, a correction method is designed to improve the detection performance, which is based on the mode probability of the current segment and its adjacent segments. The results of our detection model and correction method outperform state-of-the-art research in transportation mode detection.
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References
Openstreetmap. http://www.openstreetmap.org
Biljecki, F., Ledoux, H., Van Oosterom, P.: Transportation mode-based segmentation and classification of movement trajectories. Int. J. Geogr. Inf. Sci. 27(2), 385–407 (2013)
Chen, T., He, T.: Xgboost: extreme gradient boosting. R package version 0.4-2 (2015)
Gong, H., Chen, C., Bialostozky, E., Lawson, C.T.: A GPS/GIS method for travel mode detection in New York city. Comput. Environ. Urban Syst. 36(2), 131–139 (2012)
Haklay, M., Weber, P.: Openstreetmap: user-generated street maps. IEEE Pervasive Comput. 7(4), 12–18 (2008)
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 34. ACM (2008)
Lin, M., Hsu, W.J., Lee, Z.Q.: Detecting modes of transport from unlabelled positioning sensor data. J. Locat. Based Serv. 7(4), 272–290 (2013)
Mir, H.M., Behrang, K., Isaai, M.T., Nejat, P.: The impact of outcome framing and psychological distance of air pollution consequences on transportation mode choice. Transp. Res. Part D: Transp. Environ. 46, 328–338 (2016)
Rasmussen, T.K., Ingvardson, J.B., Halldórsdóttir, K., Nielsen, O.A.: Improved methods to deduct trip legs and mode from travel surveys using wearable gps devices: a case study from the greater copenhagen area. Comput. Environ. Urban Syst. 54, 301–313 (2015)
Tran, L.H., Nguyen, Q.V.H., Do, N.H., Yan, Z.: Robust and hierarchical stop discovery in sparse and diverse trajectories. Technical report (2011)
Xiao, G., Juan, Z., Zhang, C.: Travel mode detection based on GPS track data and bayesian networks. Comput. Environ. Urban Syst. 54, 14–22 (2015)
Ye, Y., Zheng, Y., Chen, Y., Feng, J., Xie, X.: Mining individual life pattern based on location history. In: 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pp. 1–10. IEEE (2009)
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., 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. ACM (2008)
Zheng, Y., Xie, X., Ma, W.Y.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800. ACM (2009)
Zhu, Q., Zhu, M., Li, M., Fu, M., Huang, Z., Gan, Q., Zhou, Z.: Identifying transportation modes from raw GPS data. In: Che, W., et al. (eds.) ICYCSEE 2016. CCIS, vol. 623, pp. 395–409. Springer, Singapore (2016). doi:10.1007/978-981-10-2053-7_35
Acknowledgments
The research was supported by projects of Chengdu City Science and Technology Bureau (2014-HM01-00302-SF, 2015-HM01-00484-SF). We would also like to thank all of the reviewers for their valuable and constructive comments, which greatly improved the quality of this paper.
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Liang, J. et al. (2017). An Enhanced Transportation Mode Detection Method Based on GPS Data. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_51
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DOI: https://doi.org/10.1007/978-981-10-6385-5_51
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