Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the origina...Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the original traffic flow data after wavelet decomposition.The correlation coefficients of road traffic flow data are calculated and the data compression matrix of road traffic flow is constructed.Data de-noising minimizes the interference of data to the model,while the correlation analysis of road network data realizes the prediction at the road network level.Utilizing the advantages of long short term memory(LSTM)network in time series data processing,the compression matrix is input into the constructed LSTM model for short-term traffic flow prediction.The LSTM-1 and LSTM-2 models were respectively trained by de-noising processed data and original data.Through simulation experiments,different prediction times were set,and the prediction results of the prediction model proposed in this paper were compared with those of other methods.It is found that the accuracy of the LSTM-2 model proposed in this paper increases by 10.278%on average compared with other prediction methods,and the prediction accuracy reaches 95.58%,which proves that the short-term traffic flow prediction method proposed in this paper is efficient.展开更多
To assist young learners to cultivate efficient learning strategies in the early ages, students in the current study were guided to read three authentic online storybooks and then write their own digital stories with ...To assist young learners to cultivate efficient learning strategies in the early ages, students in the current study were guided to read three authentic online storybooks and then write their own digital stories with tablet PCs. The study aims to investigate Taiwan Residents elementary school students＇ use of online reading strategies, and their relationship with students＇ performance on reading proficiency tests. The target population consisted of upper-grade learners （n = 83）. The instruments were a M-SORS （modified survey of reading strategy） questionnaire and a reading proficiency test. Major findings were as follows： （l） Students used online reading strategies at medium level; （2） problem solving strategies were proven to significantly correlate with students＇ performance on reading comprehension test; and （3） there was a significant difference between higher and lower reading proficiency learners＇ use of online reading strategies. Pedagogical implications of the findings and suggestion for future research are discussed.展开更多
基金National Natural Science Foundation of China(No.71961016)Planning Fund for the Humanities and Social Sciences of the Ministry of Education(Nos.15XJAZH002,18YJAZH148)Natural Science Foundation of Gansu Province(No.18JR3RA125)。
文摘Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the original traffic flow data after wavelet decomposition.The correlation coefficients of road traffic flow data are calculated and the data compression matrix of road traffic flow is constructed.Data de-noising minimizes the interference of data to the model,while the correlation analysis of road network data realizes the prediction at the road network level.Utilizing the advantages of long short term memory(LSTM)network in time series data processing,the compression matrix is input into the constructed LSTM model for short-term traffic flow prediction.The LSTM-1 and LSTM-2 models were respectively trained by de-noising processed data and original data.Through simulation experiments,different prediction times were set,and the prediction results of the prediction model proposed in this paper were compared with those of other methods.It is found that the accuracy of the LSTM-2 model proposed in this paper increases by 10.278%on average compared with other prediction methods,and the prediction accuracy reaches 95.58%,which proves that the short-term traffic flow prediction method proposed in this paper is efficient.
文摘To assist young learners to cultivate efficient learning strategies in the early ages, students in the current study were guided to read three authentic online storybooks and then write their own digital stories with tablet PCs. The study aims to investigate Taiwan Residents elementary school students＇ use of online reading strategies, and their relationship with students＇ performance on reading proficiency tests. The target population consisted of upper-grade learners （n = 83）. The instruments were a M-SORS （modified survey of reading strategy） questionnaire and a reading proficiency test. Major findings were as follows： （l） Students used online reading strategies at medium level; （2） problem solving strategies were proven to significantly correlate with students＇ performance on reading comprehension test; and （3） there was a significant difference between higher and lower reading proficiency learners＇ use of online reading strategies. Pedagogical implications of the findings and suggestion for future research are discussed.
