Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of ...Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.展开更多
Land subsidence is a geohazard phenomenon caused by the lowering of land elevation due to the compression of the sinking land soil body,thus creating an excessive constraint on the safe construction and sustainable de...Land subsidence is a geohazard phenomenon caused by the lowering of land elevation due to the compression of the sinking land soil body,thus creating an excessive constraint on the safe construction and sustainable development of cities.The use of accurate and efficient means for land subsidence prediction is of remarkable importance for preventing land subsidence and ensuring urban safety.Although the current time-series prediction method can accomplish relatively high accuracy,the predicted settlement points are independent of each other,and the existence of spatial dependence in the data itself is lost.In order to unlock this problem,a spatial convolutional long short-term memory neural network(ConvLSTM)based on the spatio-temporal prediction method for land subsidence is constructed.To this end,a cloud platform is employed to obtain a long time series deformation dataset from May 2017 to November 2021 in the understudied area.A convolutional structure to extract spatial features is utilized in the proposed model,and an LSTM structure is linked to the model for time-series prediction to achieve unified modeling of temporal and spatial correlation,thereby rationally predicting the land subsidence progress trend and distribution.The experimental results reveal that the prediction results of the ConvLSTM model are more accurate than those of the LSTM in about 62%of the understudied area,and the overall mean absolute error(MAE)is reduced by about 7%.The achieved results exhibit better prediction in the subsidence center region,and the spatial distribution characteristics of the subsidence data are effectively captured.The present prediction results are more consistent with the distribution of real subsidence and could provide more accurate and reasonable scientific references for subsidence prevention and control in the Beijing-Tianjin-Hebei region.展开更多
基金supported by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation“Research and System Development of Highway Asset Digitalization Technology inUse Based onHigh-PrecisionMap”(Project Number:202203)in part by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation:Research and Demonstration Application of Key Technologies for Precise Sensing of Expressway Thrown Objects(No.202204).
文摘Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.
基金National Natural Science Foundation of China,No.41930109/D010702Beijing Outstanding Young Scientist Program,No.BJJWZYJH01201910028032R&D Program of Beijing Municipal Education Commission,No.KM202210028009。
文摘Land subsidence is a geohazard phenomenon caused by the lowering of land elevation due to the compression of the sinking land soil body,thus creating an excessive constraint on the safe construction and sustainable development of cities.The use of accurate and efficient means for land subsidence prediction is of remarkable importance for preventing land subsidence and ensuring urban safety.Although the current time-series prediction method can accomplish relatively high accuracy,the predicted settlement points are independent of each other,and the existence of spatial dependence in the data itself is lost.In order to unlock this problem,a spatial convolutional long short-term memory neural network(ConvLSTM)based on the spatio-temporal prediction method for land subsidence is constructed.To this end,a cloud platform is employed to obtain a long time series deformation dataset from May 2017 to November 2021 in the understudied area.A convolutional structure to extract spatial features is utilized in the proposed model,and an LSTM structure is linked to the model for time-series prediction to achieve unified modeling of temporal and spatial correlation,thereby rationally predicting the land subsidence progress trend and distribution.The experimental results reveal that the prediction results of the ConvLSTM model are more accurate than those of the LSTM in about 62%of the understudied area,and the overall mean absolute error(MAE)is reduced by about 7%.The achieved results exhibit better prediction in the subsidence center region,and the spatial distribution characteristics of the subsidence data are effectively captured.The present prediction results are more consistent with the distribution of real subsidence and could provide more accurate and reasonable scientific references for subsidence prevention and control in the Beijing-Tianjin-Hebei region.
