Prediction method of truck travel time in open pit mines

dc.cclicenceN/Aen
dc.contributor.authorAo, Mengting
dc.contributor.authorLi, Changhe
dc.contributor.authorZeng, Sanyou
dc.contributor.authorYang, Shengxiang
dc.date.acceptance2023-04
dc.date.accessioned2023-04-26T10:36:08Z
dc.date.available2023-04-26T10:36:08Z
dc.date.issued2023-09-18
dc.description.abstractAiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver’s behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNational Natural Science Foundation of Chinaen
dc.funder.otherFundamental Research Funds for the Central Universities China University of Geosciences (Wuhan)en
dc.identifier.citationM. Ao, C. Li, and S. Yang. (2023) Prediction method of truck travel time in open pit mines based on LSTM model. Proceedings of the 42nd Chinese Control Conference (CCC), Tianjin, China, 2023, pp. 8651-8656en
dc.identifier.doihttps://doi.org/10.23919/ccc58697.2023.10240705
dc.identifier.urihttps://hdl.handle.net/2086/22764
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid62076226en
dc.projectidCUGGC02en
dc.publisherIEEE
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectTravel Time Predictionen
dc.subjectOpen Pit Trucken
dc.subjectLSTMen
dc.titlePrediction method of truck travel time in open pit minesen
dc.typeConferenceen

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