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

Dazhou Li,Qi Song,Wei Gao,Zihui Meng,Chuan Lin

doi:10.3390/s20113072

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.

Highlights

In November 2018 at the Foreign Relations Council in New York, Samuel Palmisano, CEO of IBM, put forward the concept of a “Smart Planet”
The 3-order tensor M can be decomposed by the iterative refinement in the Lagrangian function of Higher-Order Singular Value Decomposition (HOSVD) method to the kernel tensor G and a series of factor submatrixes Ui ∈ RIi ×Ri, i = 1, 2, 3
The 3-order tensor M can be decomposed by the iterative refinement in the Lagrangian function of HOSVD method to the kernel tensor G and a series of factor submatrixes Ui ∈ RIi ×Ri, i = 1,2,3

Summary

Introduction

In November 2018 at the Foreign Relations Council in New York, Samuel Palmisano, CEO of IBM, put forward the concept of a “Smart Planet”. Has stimulated the enthusiasm of countries to create smart cities. Smart cities use information technology and other high-end technologies as the cornerstone, supported by the Internet of Things (IoT) [1,2,3] and cloud computing [4,5,6]. Transparency, and networking as an essential means, on the one hand, it can reproduce the digital form of the material city. It can combine with the material city and derive a mutually beneficial urban system. Smart cities have significant ecological connotations and social responsibilities, embodying the duality of the integration of virtual and reality and the coexistence of advantages and disadvantages

Methods

Discussion

Conclusion