Prediction method of truck travel time in open pit mines based on LSTM model

Date

2023-07

Advisors

Journal Title

Journal ISSN

ISSN

1934-1768

Volume Title

Publisher

IEEE Press

Type

Conference

Peer reviewed

Yes

Abstract

Aiming 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.

Description

Keywords

TECHNOLOGY::Information technology::Computer science::Computer science

Citation

M. 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. 8651-8656

Rights

Research Institute

Institute of Artificial Intelligence (IAI)