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An Improved DBSCAN Algorithm to Analyze Taxi Pick-Up Hotspots

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6GN for Future Wireless Networks (6GN 2022)

Abstract

The DBSCAN algorithm is improved to analyze taxi pick-up hotspots. The time is divided into multiple time periods, and the load threshold and neighborhood radius are automatically extract. The accuracy of the DBSCAN algorithm for analyzing passenger areas is improved.

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References

  1. Shi, F., et al.: Sampling methods of resident trip investigation. J. Traffic Transport. Eng. 4(4), 72–75 (2004)

    Google Scholar 

  2. Wang, L., et al.: Mining frequent trajectory pattern based on vague space partition. Knowledge-Based Systems 50. Complete, 100–111 (2013)

    Google Scholar 

  3. Yu, et al.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. Spec. (2014)

    Google Scholar 

  4. Tang, J., et al.: Uncovering urban human mobility from large scale taxi GPS data. Phys. A 438, 140–153 (2015)

    Article  Google Scholar 

  5. Yuan, J., et al.: Discovering regions of different functions in a city using human mobility and POIs. ACM 186 (2012)

    Google Scholar 

  6. Cai, Y.W., Yang, B.R.: Improved DBSCAN algorithm for public bus station cluster. Comput. Eng. 34(10), 190–192 (2008)

    Google Scholar 

  7. Xu, C., Zhang, A., Chen, Y.: Traffic congestion forecasting in shanghai based on multi-period hotspot clustering. IEEE Access (99), 1–1 (2020)

    Google Scholar 

  8. Han, Y., et al.: Exploring the temporal and spatial distribution of passengers based on taxi trajectory data. Periodical of Ocean University of China (2019)

    Google Scholar 

  9. Zheng, L., et al.: Mining urban attractive areas using taxi trajectory data. Computer Applications and Software (2018)

    Google Scholar 

  10. Bao, G.: Research and system implementation of taxi passenger hotspot recommendation method. North China University of Technology (2019)

    Google Scholar 

  11. Liu, P.: Research on hotspots mining of taxi passengers based on spatial clustering and Weka platform. Jilin University (2014)

    Google Scholar 

  12. Sun, J., Guan, C., Jinhong, M.: Exploiting optimization mechanism for pick-up points recommendations. In: International Conference on Computer Systems, Electronics and Control

    Google Scholar 

  13. Chakraborty, S.: Analysis and study of incremental DBSCAN clustering algorithm. Eprint Arxiv 1(2), 2011 (2014)

    Google Scholar 

  14. Chen, M., Gao, X.D., Li, H.F.: Parallel DBSCAN with Priority R-tree. In: 2010 The 2nd IEEE International Conference on IEEE Information Management and Engineering (ICIME) (2010)

    Google Scholar 

  15. Huang, Z.: Mining and recommendation of customer-seeking areas based on taxi trajectories. Hangzhou Dianzi University (2020)

    Google Scholar 

  16. Yi, L.I., et al.: A weighted centroid localization algorithm based on DBSCAN clustering point density. J. Henan Univ. Sci. Technol. (Natl. Sci.) (2018)

    Google Scholar 

  17. Ester, M., et al.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. AAAI Press (1996)

    Google Scholar 

  18. Murakami, E., Wagner, D.: Can using global positioning system (GPS) improve trip reporting? Transport. Res. Part C Emerg. Technol. 7(2–3), 149–165 (1999)

    Article  Google Scholar 

  19. Rousseeuw, Peter J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7

    Article  MATH  Google Scholar 

Download references

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 62071157, National Key Research and Development Programme 2022YFD2000500 and Natural Science Foundation of Heilongjiang Province under Grant YQ2019F011.

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Correspondence to Zizhen Wang .

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zheng, W., Cheng, Y., Li, N., Wang, Z., Li, A. (2023). An Improved DBSCAN Algorithm to Analyze Taxi Pick-Up Hotspots. In: Li, A., Shi, Y., Xi, L. (eds) 6GN for Future Wireless Networks. 6GN 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-36011-4_16

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  • DOI: https://doi.org/10.1007/978-3-031-36011-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36010-7

  • Online ISBN: 978-3-031-36011-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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