Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City

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 “last mile” 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.

A Unified Fourth-Order Tensor-Based Smart Community System

“…Moreover, as our proposed fourth-order tensor model includes more data, particularly human activity data, gains could be achieved from the proposed scheme compared to conventional methods based on third-order tensors. A representative third-order tensor-based scheme was proposed in [19] to forecast the rental data of urban public bicycles to assist redistribution. The three dimensions of the third-order tensor in [19] are time, location and numbers of bicycles rented out and returned back, while the effect of human activity is overlooked.…”

“…A representative third-order tensor-based scheme was proposed in [19] to forecast the rental data of urban public bicycles to assist redistribution. The three dimensions of the third-order tensor in [19] are time, location and numbers of bicycles rented out and returned back, while the effect of human activity is overlooked. It should be noted that human activity plays a key role in the usage of public bicycles.…”

“…Based on the above four-dimensional model of a human-centered smart community system, a fourth-order tensor model that represents object, time, location and human activity is proposed in this paper to store all the data collected by IoT devices. This is motivated by the appealing features of the tensor to manage heterogeneous and enormous data [ 17 , 18 ], which has also been shown to be a powerful tool for public bicycle rental forecasting [ 19 ] and breathing monitoring [ 20 ], to name a few. As all the data are now formalized into the proposed fourth-order tensor model, a unified smart community system can be proposed, where tensor data can be truncated and extracted to fit various application purposes.…”

Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning

“…The results showed the model’s effectiveness and accuracy can be improved through combining the LSTM and GRU architectures together. In a study by Li et al [ 20 ], a hybrid LSTM based model was proposed to simultaneously predict short-term bike demand for all bike-sharing stations in the entire city, modeled as a whole. The proposed method effectively improved the prediction performance by capturing the spatiotemporal dependencies.…”

Research on the Application of Time Convolution Series in Futures Price Forecasting

“…The attribute recognition method is a supervised pattern method. Its main feature is that it reflects the importance of each pattern index and plays an important role in pattern recognition [5]. The typical unbalanced data processing method is sampling method [6].…”