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Logistics Vehicle Travel Preference of Interest Points Based on Speed and Accessory State

  • Shengwu Xiong
  • Li Kuang
  • Pengfei DuanEmail author
  • Wei Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9864)

Abstract

In a crowded city, directions and speed of vehicles are usually changed arbitrarily. Analyzing travel preferences of vehicle has become a focus of research as it helps to classify region of interest in city and can be used in personalized recommendation and many other areas of application. In this paper, a travel identification method based on vehicle speed and Accessory (ACC) State is proposed. Continuously classifying and merging the trajectory points in GPS data stream, the travel activities of vehicle is extracted. It can provide a basis of data for the research on hot spots and support the research and application of vehicle trajectory data mining in areas of intelligent transportation and logistics.

Keywords

Vehicle speed ACC state Travel identification Hot spots Logistics vehicle 

Notes

Acknowledgments

This work was supported in part by the National High-tech R&D Program of China (863 Program) under Grant No. 2015AA015403, Science & Technology Pillar Program of Hubei Province under Grant No. 2014BAA146, Nature Science Foundation of Hubei Province under Grant No. 2015CFA059, Hubei Key Laboratory of Transportation Internet of Things under Grant No. 2015III015-B03 and CERNET Innovation Project under Grant No. NGII20151006.

References

  1. 1.
    Deng, Z., Ji, M., Chen, W.: Coupling passive GPS tracking and web-based travel surveys. J. Transp. Syst. Eng. Inf. Technol. 10(2), 178–183 (2009)Google Scholar
  2. 2.
    Stopher, P., FitzGerald, C., Zhang, J.: Search for a global positioning system device to measure personal travel. Transp. Res. Part C 16, 350–369 (2008)CrossRefGoogle Scholar
  3. 3.
    Zhang, B.: Research on the Simplification and Semantic Enhancement of GPS Temporal and Spatial Trajectory Data for Traffic Travel Survey. East China Normal University, Shanghai (2011)Google Scholar
  4. 4.
    Zhou, C., Frankowski, D., Ludford, P., et al.: Discovering personal gazetteers: an interactive clustering approach, pp. 266–273. ACM (2004)Google Scholar
  5. 5.
    Tietbohl, A., Bogorny, V., Kuijpers, B., et al.: A clustering-based approach for discovering interesting places in trajectories. In: SAC, pp. 863–868 (2008)Google Scholar
  6. 6.
    Zhang, J., Qiu, P., Xu, Z.: A method to identify trip based on the mobile phone positioning. J. Wuhan Univ. Technol. (Transp. Sci. Eng.) 37(5), 934–938 (2013)Google Scholar
  7. 7.
    Zou, Y., Wan, J., Xia, Y.: LBSN user movement trajectory clustering mining method based on road network. Appl. Res. Comput. 08(8), 102–110 (2013)Google Scholar
  8. 8.
    Xiao, Y., Zhang, Z., Yang, W.: Users’ mobility behaviours mining algorithm based on GPS trajectory. Comput. Appl. Softw. 32(11), 83–87 (2015)Google Scholar
  9. 9.
    Yuan, J., Zheng, Y., Xie, X.: Discovering regions of different functions in a city using human mobility and POIs. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 186–194 (2012)Google Scholar
  10. 10.
    Xue, A., Zhang, R., Zheng, Y., et al.: Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In: IEEE International Conference on Data Engineering, pp. 254–265 (2013)Google Scholar
  11. 11.
    Yuan, J., Zheng, Y., Xie, X., et al.: T-Drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220–232 (2013)CrossRefGoogle Scholar
  12. 12.
    Zheng, Y., Xie, X.: Learning travel recommendations from user-generated GPS traces. ACM Trans. Intell. Syst. Technol. 2(1), 389–396 (2011)CrossRefGoogle Scholar
  13. 13.
    Zheng, V., Zheng, Y., Xie, X., et al.: Collaborative location and activity recommendations with GPS history data. In: Proceeding of the 19th International Conference on World Wide Web (2010)Google Scholar
  14. 14.
    Ma, S., Zheng, Y., Wolfson, O.: T-share: a large-scale dynamic taxi ridesharing service. In: IEEE International Conference on Data Engineering, pp. 410–421 (2013)Google Scholar
  15. 15.
    Liu, Y., Kang, C., Gao, S., et al.: Understanding intra-urban trip patterns from taxi trajectory data. J. Geogr. Syst. 14(4), 463–483 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Yuan, N., Zheng, Y., Zhang, L., et al.: T-Finder: a recommender system for finding passengers and vacant taxis. IEEE Trans. Knowl. Data Eng. 25(10), 2390–2403 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Shengwu Xiong
    • 1
    • 2
  • Li Kuang
    • 1
  • Pengfei Duan
    • 1
    • 2
    Email author
  • Wei Shi
    • 1
  1. 1.School of Computer Science and TechnologyWuhan University of TechnologyWuhanPeople’s Republic of China
  2. 2.Hubei Key Laboratory of Transportation Internet of ThingsWuhanPeople’s Republic of China

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