To accurately analyze the fluctuation range of time-varying differences in metro-to-bus transfer passenger flows,the application of a probabilistic interval prediction model is proposed to predict transfer passenger f...To accurately analyze the fluctuation range of time-varying differences in metro-to-bus transfer passenger flows,the application of a probabilistic interval prediction model is proposed to predict transfer passenger flows.First,bus and metro data are processed and matched by association to construct the basis for public transport trip chain extraction.Second,a reasonable matching threshold method to discriminate the transfer relationship is used to extract the public transport trip chain,and the basic characteristics of the trip based on the trip chain are analyzed to obtain the metro-to-bus transfer passenger flow.Third,to address the problem of low accuracy of point prediction,the DeepAR model is proposed to conduct interval prediction,where the input is the interchange passenger flow,the output is the predicted median and interval of passenger flow,and the prediction scenarios are weekday,non-workday,and weekday morning and evening peaks.Fourth,to reduce the prediction error,a combined particle swarm optimization(PSO)-DeepAR model is constructed using the PSO to optimize the DeepAR model.Finally,data from the Beijing Xizhimen subway station are used for validation,and results show that the PSO-DeepAR model has high prediction accuracy,with a 90%confidence interval coverage of up to 93.6%.展开更多
Snowline change and snow cover distribution patterns are still poorly understood in steep alpine basins of the Qilian Mountainous region because fast changes in snow cover cannot be observed by current sensing methods...Snowline change and snow cover distribution patterns are still poorly understood in steep alpine basins of the Qilian Mountainous region because fast changes in snow cover cannot be observed by current sensing methods due to their short time scale. To address this issue of daily snowline and snow cover observations, a ground- based EOS 7D camera and four infrared digital hunting video cameras （LTL5210A） were installed around the Hulugou river basin （HRB） in the Qilian Mountains along northeastern margin of the Tibetan Plateau （38°15′54″N, 99°52′53″E） in September 2011. Pictures taken with the EOS 7D camera were georeferenced and the data from four LIL521oA cameras and snow depth sensors were used to assist snow cover estimation. The results showed that the time-lapse photography can be very useful and precise for monitoring snowline and snow cover in mountainous regions. The snowline and snow cover evolution at this basin can be precisely captured at daily scale. In HRB snow cover is mainly established after October, and the maximum snow cover appeared during February and March. The consistent rise of the snowline and decrease in snow cover appeared after middle part of March. This melt process is strongly associated with air temperature increase.展开更多
Using daily precipitation data from weather stations in China, the variations in the contribution of extreme precipitation to the total precipitation are analyzed. It is found that extreme precipitation accounts for a...Using daily precipitation data from weather stations in China, the variations in the contribution of extreme precipitation to the total precipitation are analyzed. It is found that extreme precipitation accounts for approximately one third of the total precipitation based on the overall mean for China. Over the past half century, extreme precipitation has played a dominant role in the year-to-year variability of the total precipitation. On the decadal time scale, the extreme precipitation makes different contributions to the wetting and drying regions of China. The wetting trends of particular regions are mainly attributed to increases in extreme precipitation; in contrast, the drying trends of other regions are mainly due to decreases in non-extreme precipitation.展开更多
.GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN alg....GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN algorithm. The algorithm firstly get the network center nodes set, then use the shortest paths between center nodes and other nodes to calculate the edge betweenness, and then use incremental module degree as the algorithm terminates standard. Experiments show that, the new algorithm not only ensures accuracy of network community division, but also greatly reduced the time complexity, and improves the efficiency of community division.展开更多
基金The National Key Research and Development Program of China(No.2019YFB160-0200)the National Natural Science Foundation of China(No.71871011,71890972/71890970)。
文摘To accurately analyze the fluctuation range of time-varying differences in metro-to-bus transfer passenger flows,the application of a probabilistic interval prediction model is proposed to predict transfer passenger flows.First,bus and metro data are processed and matched by association to construct the basis for public transport trip chain extraction.Second,a reasonable matching threshold method to discriminate the transfer relationship is used to extract the public transport trip chain,and the basic characteristics of the trip based on the trip chain are analyzed to obtain the metro-to-bus transfer passenger flow.Third,to address the problem of low accuracy of point prediction,the DeepAR model is proposed to conduct interval prediction,where the input is the interchange passenger flow,the output is the predicted median and interval of passenger flow,and the prediction scenarios are weekday,non-workday,and weekday morning and evening peaks.Fourth,to reduce the prediction error,a combined particle swarm optimization(PSO)-DeepAR model is constructed using the PSO to optimize the DeepAR model.Finally,data from the Beijing Xizhimen subway station are used for validation,and results show that the PSO-DeepAR model has high prediction accuracy,with a 90%confidence interval coverage of up to 93.6%.
基金supported by the National Natural Sciences Foundation of China (Grant Nos. 41401078, 91025011, 41222001)National Basic Research Program of China (2013CBA01806)
文摘Snowline change and snow cover distribution patterns are still poorly understood in steep alpine basins of the Qilian Mountainous region because fast changes in snow cover cannot be observed by current sensing methods due to their short time scale. To address this issue of daily snowline and snow cover observations, a ground- based EOS 7D camera and four infrared digital hunting video cameras （LTL5210A） were installed around the Hulugou river basin （HRB） in the Qilian Mountains along northeastern margin of the Tibetan Plateau （38°15′54″N, 99°52′53″E） in September 2011. Pictures taken with the EOS 7D camera were georeferenced and the data from four LIL521oA cameras and snow depth sensors were used to assist snow cover estimation. The results showed that the time-lapse photography can be very useful and precise for monitoring snowline and snow cover in mountainous regions. The snowline and snow cover evolution at this basin can be precisely captured at daily scale. In HRB snow cover is mainly established after October, and the maximum snow cover appeared during February and March. The consistent rise of the snowline and decrease in snow cover appeared after middle part of March. This melt process is strongly associated with air temperature increase.
基金supported by the"Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues" of the Chinese Academy of Sciences(Grant No. XDA05090306)the National Basic Research Programof China(Grant No.2009CB421406)the Chinese Academy of Sciences-Common wealth Scientific and Industrial Research Organisation Cooperative Research Program(Grant No.GJHZ1223)
文摘Using daily precipitation data from weather stations in China, the variations in the contribution of extreme precipitation to the total precipitation are analyzed. It is found that extreme precipitation accounts for approximately one third of the total precipitation based on the overall mean for China. Over the past half century, extreme precipitation has played a dominant role in the year-to-year variability of the total precipitation. On the decadal time scale, the extreme precipitation makes different contributions to the wetting and drying regions of China. The wetting trends of particular regions are mainly attributed to increases in extreme precipitation; in contrast, the drying trends of other regions are mainly due to decreases in non-extreme precipitation.
文摘.GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN algorithm. The algorithm firstly get the network center nodes set, then use the shortest paths between center nodes and other nodes to calculate the edge betweenness, and then use incremental module degree as the algorithm terminates standard. Experiments show that, the new algorithm not only ensures accuracy of network community division, but also greatly reduced the time complexity, and improves the efficiency of community division.
