Short-term Bus Passenger Flow Prediction Based on Convolutional Long-short-term Memory Network
To address the problem that the traditional short-time passenger flow prediction method does not consider the temporal characteristics similarity between the inter-temporal passenger flows,a short-time passenger flow prediction model k-CNN-LSTM is proposed by combining the improved k-means clustering algorithm with the CNN and the LSTM.The k-means is used to cluster the intertemporal time-series data,the k-value is determined by using the gap-statistic,and a traffic flow matrix model is constructed.A CNN-LSTM network is used to process the short-time passenger flows with spatial and temporal characteristics.The model is tested and parameter tuned by the real dataset.The test results show the model is able to predict the spatially correlated data more accurately.
CNNLSTMspatiotemporal data predictionk-means clusteringpassenger flow prediction
万方数据
维普
