Mingyuan Li1,*, Yuanli Gu1, Qingqiao Geng2, Hongru Yu1
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3877-3896, 2024, DOI:10.32604/cmc.2024.047765
Abstract This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors. The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Convolutional Long Short Term Memory Neural Network (ConvLSTM) to predict short-term taxi travel demand. The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components, capturing sequence characteristics at different time scales and frequencies. Based on the sample entropy value of components, secondary processing of more… More >
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