Advertisement

Construction Area Identification Method Based on Spatial-Temporal Trajectory of Slag Truck

  • Jinjuan Wen
  • Fumin ZouEmail author
  • Lyuchao Liao
  • Rong Hu
  • Zhiyuan Hu
  • Zhihui Chen
  • Qiqin Cai
  • Jierui Liu
Conference paper
  • 24 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)

Abstract

The enclosure construction is easy to cause traffic congestion in the process, so timely determination of it is crucial to alleviate traffic congestion. At present, most existing methods are either susceptible to obstacles such as clouds and fog, or only suitable for small-scale areas. To address these problems, this paper proposes an effective automatic identification method, which mines the spatial-temporal trajectory data of common engineering vehicles such as slag trucks to automatically determine the construction area. To reduce the memory consumption and the influence of the two parameters ε (field radius) and MinPts (domain density threshold) on the clustering results, we first divide the trajectory points, match them to the grid and generate cluster candidate sets by extracting High-density grid base on the preset density threshold. Then, the DBSCAN algorithm is used to identify the construction areas, which greatly shorten the running time. The experimental results show that the method is effective through the verification of ArcGIS & Google Earth.

Keywords

Construction area Slag truck Spatial-temporal trajectory data DBSCAN algorithm ArcGIS & Google Earth 

Notes

Acknowledgment

This work was supported in part by projects of Provincial Economic and Trade Commission (Rong Finance Enterprise (refers) [2018] 41) Research and Application of Urban Traffic accident Express and violation report system based on vehicle networking Technology, project of Municipal Bureau of Science and Technology (2019-G-40) Research and Application of key Technology of’Beautiful Travel’ Fuzhou Traffic smooth Service system, project of Institute Research and Development Fund (GY-Z17151, GY-Z13125, GY-Z160064), project of General Project of Provincial Natural Science Found (2019I0019), project of General Project of Science and Technology at Education Department level (JAT170368).

References

  1. 1.
    Behnam, A., Wickramasinghe, D.C., Ghaffar, M.A., et al.: Automated progress monitoring system for linear infrastructure projects using satellite re-mote sensing. Autom. Constr. 68, 114–127 (2016)CrossRefGoogle Scholar
  2. 2.
    Zhu, D., Wang, B., Zhang, L.: Target detection in remote sensing images: a new method based on two-way saliency. IEEE Geosci. Remote Sens. Lett. 12(5), 1096–1100 (2015)CrossRefGoogle Scholar
  3. 3.
    Valero, S., Morin, D., Inglada, J., et al.: Production of a dynamic cropland mask by processing remote sensing image series at high temporal and spatial resolutions. Remote Sens. 8(1), 55 (2016)CrossRefGoogle Scholar
  4. 4.
    Ham, Y., Han, K., Lin, J., et al.: Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works. Vis. Eng. 4(1), 1 (2016)CrossRefGoogle Scholar
  5. 5.
    Yu, B., Niu, W., Wang, L., et al.: A tower crane extraction method based on multi-scale adaptive morphology. Remote Sens. Technol. Appl. 28(2), 240–244 (2013)Google Scholar
  6. 6.
    Chen, Y., Zhang, L., Hai, J.: Research progress and trend of big data-driven intelligent transportation system. Chin. J. Internet Things (1), 56–63 (2018)Google Scholar
  7. 7.
    Zhang, L., Xin, Z., Zhaohui, J., et al.: Mining urban attractive areas using taxi trajectory data. Comput. Appl. Softw. (1), 1–8 (2018)Google Scholar
  8. 8.
    Liu, S., Wang, S.: Trajectory community discovery and recommendation by multi-source dif-fusion modeling. IEEE Trans. Knowl. Data Eng. 29(4), 898–911 (2017)CrossRefGoogle Scholar
  9. 9.
    Lvchao, L., Xinhua, J., Fumin, Z.: A spectral clustering method for big trajectory data mining with latent semantic correlation. Acta Electron. Sinica 43(5), 956–964 (2015)Google Scholar
  10. 10.
    Zhi, L., Huiping, L., Dapeng, Z., et al.: Business circle population mobility statistics based on mobile trajectory data. J. East China Normal Univ. (Nat. Sci.) (04), 97–113 + 138 (2017)Google Scholar
  11. 11.
    Hu, R., Chiu, Y.C., Hsieh, C.W., et al.: Mass rapid transit system passenger traffic forecast using a re-sample recurrent neural network. J. Adv. Transp. 2019 (2019)Google Scholar
  12. 12.
    Deren, L., Jun, M., Zhenfeng, S.: Discuss of space-time big data and its application. Satellite Application (09), 7–11 (2015)Google Scholar
  13. 13.
    Zou, F., Liao, L., Jiang, X., Lai, H.: An automatic recognition approach for traffic congestion states based on traffic video. Highw. Traffic Sci. Technol. Engl. Ed. 8(2), 72–80 (2014)Google Scholar
  14. 14.
    Meiwei, H., Hualin, D., Kun, H.: Optimization of density-based K-means algorithm in trajectory data clustering. J. Comput. Appl. 10, 2946–2951 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jinjuan Wen
    • 1
    • 2
  • Fumin Zou
    • 1
    • 2
    Email author
  • Lyuchao Liao
    • 1
    • 2
  • Rong Hu
    • 1
    • 2
  • Zhiyuan Hu
    • 1
    • 2
  • Zhihui Chen
    • 1
    • 2
  • Qiqin Cai
    • 1
    • 2
  • Jierui Liu
    • 1
    • 2
  1. 1.Fujian Key Lab for Automotive Electronics and Electric DriveFujian University of TechnologyFuzhouChina
  2. 2.Fujian Provincial Big Data Institute for Intelligent TransportationFujian University of TechnologyFuzhouChina

Personalised recommendations