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Identifying Transportation Modes Using Gradient Boosting Decision Tree

  • Xin Fu
  • Dong Wang
  • Hengcai Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

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.

Keywords

Trajectories Transportation mode GBDT Pattern recognition Geographical features 

Notes

Acknowledgments

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.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Resources and EnvironmentUniversity of JinanJinanChina
  2. 2.School of Information Science and EngineeringUniversity of JinanJinanChina
  3. 3.State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

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