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.
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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).
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Wen, J. et al. (2020). Construction Area Identification Method Based on Spatial-Temporal Trajectory of Slag Truck. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_21
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DOI: https://doi.org/10.1007/978-981-15-3308-2_21
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