Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City
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 d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/11/3072 |
| _version_ | 1797566683639971840 |
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| author | Dazhou Li Chuan Lin Wei Gao Zihui Meng Qi Song |
| author_facet | Dazhou Li Chuan Lin Wei Gao Zihui Meng Qi Song |
| author_sort | Dazhou Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:30:58Z |
| format | Article |
| id | doaj.art-b45c82eb984e4c7e81ffd4050c71c472 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:30:58Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b45c82eb984e4c7e81ffd4050c71c4722023-11-20T02:08:57ZengMDPI AGSensors1424-82202020-05-012011307210.3390/s20113072Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart CityDazhou Li0Chuan Lin1Wei Gao2Zihui Meng3Qi Song4College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110016, ChinaKey Laboratory for Ubiquitous Network and Service Software of Liaoning province, Dalian University of Technology, Dalian 116024, ChinaCollege of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110016, ChinaCollege of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110016, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110819, ChinaAs 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.https://www.mdpi.com/1424-8220/20/11/3072smart cityIoTsmeteorologicalblockinglong short-term memory (LSTM)high order singular value decomposition (HOSVD) |
| spellingShingle | Dazhou Li Chuan Lin Wei Gao Zihui Meng Qi Song Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City Sensors smart city IoTs meteorological blocking long short-term memory (LSTM) high order singular value decomposition (HOSVD) |
| title | Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City |
| title_full | Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City |
| title_fullStr | Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City |
| title_full_unstemmed | Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City |
| title_short | Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City |
| title_sort | short term rental forecast of urban public bicycle based on the hosvd lstm model in smart city |
| topic | smart city IoTs meteorological blocking long short-term memory (LSTM) high order singular value decomposition (HOSVD) |
| url | https://www.mdpi.com/1424-8220/20/11/3072 |
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