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Identification of workstations in earthwork operations from vehicle GPS data
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.ORCID iD: 0000-0002-4106-3126
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. Department of Civil and Environmental Engineering, Northeastern University, 360 Huntington Avenue, Boston, USA.
2017 (English)In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 83, p. 237-246Article in journal (Refereed) Published
Abstract [en]

The paper proposes a methodology for the identification of workstations in earthwork operations based on GPS traces from construction vehicles. The model incorporates relevant information extracted from the GPS data to infer locations of different workstations as probability distributions over the environment. Monitoring of workstation locations may support map inference for generating and continuously updating the layout and road network topology of the construction environment. A case study is conducted at a complex earthwork site in Sweden. The workstation identification methodology is used to infer the locations of loading stations based on vehicle speeds and interactions between vehicles, and the locations of dumping stations based on vehicle turning patterns. The results show that the proposed method is able to identify workstations in the earthwork environment efficiently and in sufficient detail.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2017. Vol. 83, p. 237-246
Keywords [en]
Earthwork operations, Global Positioning System (GPS), Location detection, Probabilistic model, Kernel density estimation
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-215781DOI: 10.1016/j.autcon.2017.08.023ISI: 000411533400020Scopus ID: 2-s2.0-85027503397OAI: oai:DiVA.org:kth-215781DiVA, id: diva2:1151165
Note

QC 20171023

Available from: 2017-10-23 Created: 2017-10-23 Last updated: 2017-10-23Bibliographically approved

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Fu, JialiJenelius, Erik

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Output format
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