Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Sign In or Register Submit

PDF
PUBLISHED
25.04.2023
DOI
https://doi.org/10.7307/ptt.v35i2.106
ISSUE
2023 (Vol 35), Issue 2
LICENSE
Copyright (c) 2024 Siyuan Sun, Ronghui Bi, Zongyao Wang, Yu Ji
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Loading and Unloading Points Identification Based on Freight Trajectory Big Data and Clustering Method

Authors:Siyuan Sun, Ronghui Bi, Zongyao Wang, Yu Ji

Abstract

Based on the GPS trajectory data of a freight enterprise in Dalian, China, this paper studies the identification of loading and unloading points by a clustering algorithm. Firstly, by analysing the characteristics of freight loading and unloading behaviour, combined with the spatial and temporal distribution characteristics of truck GPS trajectory data, three characteristic variables of the number of trucks passing through a certain place, the average speed of trucks and the average stay time of trucks in the place are extracted. Then, the clustering algorithm and visual analysis are used to obtain the target cluster, and the POI language of the geographic information is obtained according to the points in the target cluster. The meaning information is crawled to accurately identify the result of the freight loading point. Finally, two classical clustering algorithms, K-means and GMM, are evaluated and compared. The results show that the identification method designed in this paper finally identifies 2,320 freight loading and unloading points from 11,406,000 trajectory data, which can realise the accurate extraction of freight loading and unloading points.

Keywords:loading and unloading points identification, cluster analysis, GPS truck tracking, K-means algorithm, GMM algorithm, data mining

References

  1. [1] Liu S, Chen G, Wei L, Li G. A novel compression approach for truck GPS trajectory data. IET Intelligent Transport Systems. 2021;15:74–83. DOI: 10.1049/itr2.12005.
  2. [2] Duan M, Qi G, Guan W, Guo R. Comprehending and analyzing multiday trip-chaining patterns of freight vehicles using a multiscale method with prolonged trajectory data. Journal of Transportation Engineering, Part A: Systems. 2020;146:04020070. DOI: 10.1061/JTEPBS.0000392.
  3. [3] Xiao ZP, Zou HX, Sun YH. Using GPS data to visualize the intra-city freight mobility—the case of Shenzhen. Journal of Human Settlements in West China. 2017;32:9–15. DOI: 10.13791/j.cnki.hsfwest.20170102.
  4. [4] Csendes B, Albert G, Szander N, Munkácsy A. Where truck drivers stop–application of vehicle tracking data for the identification of rest locations and driving patterns. Promet – Traffic&Transportation. 2021;33:821–32. DOI: 10.7307/ptt. v33i6.3962.
  5. [5] Gan M, Nie YM, Liu X, Zhu D. Whereabouts of truckers: An empirical study of predictability. Transportation Research Part C: Emerging Technologies. 2019;104:184–95. DOI: 10.1016/j.trc.2019.04.020.
  6. [6] Gingerich K. Studying regional and cross border freight movement activities with truck GPS big data. PhD Thesis. University of Windsor (Canada); 2017.
  7. [7] Gingerich K, Maoh H, Anderson W. Classifying the purpose of stopped truck events: An application of entropy to GPS data. Transportation Research Part C: Emerging Technologies. 2016;64:17–27. DOI: 10.1016/j.trc.2016.01.002.
  8. [8] Thakur A, et al. Development of algorithms to convert large streams of truck GPS data into truck trips. Transportation Research Record. 2015;2529:66–73. DOI: 10.3141/2529-07.
  9. [9] Yang X, Sun Z, Ban X J, Holguín-Veras J. Urban freight delivery stop identification with GPS data. Transportation Research Record. 2014;2411:55–61. DOI: 10.3141/2411-07.
  10. [10] Du J, Aultman-Hall L. Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues. Transportation Research Part A: Policy and Practice. 2007;41:220–32. DOI: 10.1016/j.tra.2006.05.001.
  11. [11] Hess S, Quddus M, Rieser-Schüssler N, Daly A. Developing advanced route choice models for heavy goods vehicles using GPS data. Transportation Research Part E: Logistics and Transportation Review. 2015;77:29–44. DOI: 10.1016/j.tre.2015.01.010.
  12. [12] Bernardin Jr VL, Steven T, Jeffery S. Expanding truck GPS-based passive origin-destination data in Iowa and Tennessee. TRB 94th Annual Meeting Compendium of Papers. 2015.
  13. [13] Bassok A, McCormack ED, Outwater ML, Ta C. Use of truck GPS data for freight forecasting. TRB 90th Annual Meeting Compendium of Papers. 2011.
  14. [14] Yang Y, et al. Identifying intracity freight trip ends from heavy truck GPS trajectories. Transportation Research Part C: Emerging Technologies. 2022;136:103564. DOI: 10.1016/j.trc.2022.103564.
  15. [15] Cheng Z, Wang W, Lu J, Xing X. Classifying the traffic state of urban expressways: A machine-learning approach. Transportation Research Part A: Policy and Practice. 2020;137:411–28. DOI: 10.1016/j.tra.2018.10.035.
  16. [16] Yuan Y, et al. Traffic state classification and prediction based on trajectory data. Journal of Intelligent Transportation Systems. 2021:1–15. DOI: 10.1080/15472450.2021.1955210.
  17. [17] Gan M, Qing S-D, Liu X-B, Li D-D. Review on application of truck trajectory data in highway freight system. Journal of Transportation Systems Engineering and Information Technology. 2021;21:91. DOI: 10.16097/j.cnki.1009-6744.2021.05.009.
  18. [18] Bohte W, Maat K. Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands. Transportation Research Part C: Emerging Technologies. 2009;17:285–97. DOI: 10.1016/j.trc.2008.11.004.
  19. [19] Zheng Y. Trajectory data mining: An overview. ACM Transactions on Intelligent Systems and Technology (TIST). 2015;6:1–41. DOI: 10.1145/2743025.
  20. [20] Feng Z, Zhu Y. A survey on trajectory data mining: Techniques and applications. IEEE Access. 2016;4:2056–67. DOI: 10.1109/ACCESS.2016.2553681.
  21. [21] Portugal I, Alencar P, Cowan D. A framework for spatial-temporal trajectory cluster analysis based on dynamic relationships. IEEE Access. 2020;8:169775–93. DOI: 10.1109/ACCESS.2020.3023376.
  22. [22] MacQueen J. Classification and analysis of multivariate observations. 5th Berkeley Symp. Math. Statist. Probability. 1967. p. 281–97.
  23. [23] Yan XD, et al. Regional division and hierarchical structure of metropolitan area based on carpooling data and clustering method. Journal of Transportation Systems Engineering and Information Technology. 2021;21:30. DOI: 10.16097/j.cnki.1009-6744.2021.04.004.
  24. [24] You F, et al. The trajectory of densely tracked the trajectory of the multi-target tracking and the semantic perception of sports. Transportation System Engineering and Information. 2021;21:42. DOI: 10.16097/j.cnki.1009-6744.2021.06.006.
  25. [25] Liu T, et al. Study on driving style clustering based on K-means and Gaussian mixture model. China Safety Science Journal. 2019;29:40. DOI: 10.16265/j.cnki.issn1003-3033.2019.12.007.
  26. [26] Chen Y, et al. Gaussian mixture clustering algorithm combining elbow method and expectation-maximization for power system customer segmentation. Journal of Computer Applications. 2020;40:3217. DOI: 10.11772/j.issn.1001-9081.2020050672.
  27. [27] Zhang XH, et al. Research on the evaluation index of duty cycle-based clustering algorithm. Computer Engineering and Applications. 2022;58:175–81. DOI: 10.3778/j.issn.1002-8331.2007-0298.
  28. [28] Jin Y, et al. Intelligent on-demand design of phononic metamaterials. Nanophotonics. 2022;11(3):439-460. DOI: 10.1515/nanoph-2021-0639.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Research on the Efficiency of Major Airports in China’s Six Major Airport Cluster: Based on Common Frontier Super-Efficiency DEA and Malmquist MethodFactors Influencing Sustained Use of Shared E-Scooter Services in Urban TaiwanA Study on the Impact of Overtaking Lane-Changing Behaviour in Expressway Interchange Weaving AreasWireless Channel Propagation Model for Inland Waterway Bridge Scenario

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal