@article{ref1,
title="Short-term rental forecast of urban public bicycle based on the HOSVD-LSTM  model in smart city",
journal="Sensors (Basel)",
year="2020",
author="Li, Dazhou and Lin, Chuan and Gao, Wei and Meng, Zihui and Song, Qi",
volume="20",
number="11",
pages="e3072-e3072",
abstract="As a kind of transportation in a smart city, urban public bicycles have been adopted by major cities and bear the heavy responsibility of the &quot;last mile&quot; of urban public transportation. At present, the main problem of the urban public bicycle system is that it is difficult for users to rent a bike during peak h, and real-time monitoring cannot be solved adequately. Therefore, predicting the demand for bicycles in a certain period and performing redistribution in advance is of great significance for solving the lag of bicycle system scheduling with the help of IoT. Based on the HOSVD-LSTM prediction model, a prediction model of urban public bicycles based on the hybrid model is proposed by transforming the source data (multiple time series) into a high-order tensor time series. Furthermore, it uses the tensor decomposition technology (HOSVD decomposition) to extract new features (kernel tenor) from higher-order tensors. At the same time, these kernel tenors are directly used to train tensor LSTM models to obtain new kernel tenors. The inverse tensor decomposition and high-dimensional, multidimensional, and tensor dimensionality reduction were introduced. The new kernel tenor obtains the predicted value of the source sequence. Then the bicycle rental amount is predicted.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s20113072",
url="http://dx.doi.org/10.3390/s20113072"
}