Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather
To help related operators to allocate and dispatch the number of bike-sharing and provide good guidance for setting up electronic fences, this paper proposes a spatiotemporal graph convolution network prediction model (SGCNPM) with multiple factors to enhance the accuracy of predicting the demand fo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/7407748 |
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| _version_ | 1832551087723249664 |
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| author | Yang Yang Xin Shao Yuting Zhu Enjian Yao Dongmei Liu Feng Zhao |
| author_facet | Yang Yang Xin Shao Yuting Zhu Enjian Yao Dongmei Liu Feng Zhao |
| author_sort | Yang Yang |
| collection | DOAJ |
| description | To help related operators to allocate and dispatch the number of bike-sharing and provide good guidance for setting up electronic fences, this paper proposes a spatiotemporal graph convolution network prediction model (SGCNPM) with multiple factors to enhance the accuracy of predicting the demand for bike-sharing. First, we consider time, built environment, and weather. We use a multigraph convolution network (GCN) to model the built environment, utilize a long short-term memory (LSTM) network to extract temporal features, and utilize a fully connected network (FCN) to model weather influence. We construct SGCNPM which can effectively fuse GCN, LSTM, and FCN, thus creating a prediction method considering the influence of multiple factors. The results of the real case in Tianjin, China, show that the proposed model can perform well in improving prediction accuracy. Also, we analyze the influence of factors on model prediction results in different periods. |
| format | Article |
| id | doaj-art-f553f72a44ec4434a5c06f34ad3f79f3 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-f553f72a44ec4434a5c06f34ad3f79f32025-02-03T06:05:04ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/7407748Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and WeatherYang Yang0Xin Shao1Yuting Zhu2Enjian Yao3Dongmei Liu4Feng Zhao5Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive TransportKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive TransportSchool of E-Business and LogisticsKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive TransportResearch and Development Center of Transport Industry of Big Data Processing Technologies and Application for RIOH High Science and Technology GroupTianjin Intelligent Traffic Operation Monitoring CenterTo help related operators to allocate and dispatch the number of bike-sharing and provide good guidance for setting up electronic fences, this paper proposes a spatiotemporal graph convolution network prediction model (SGCNPM) with multiple factors to enhance the accuracy of predicting the demand for bike-sharing. First, we consider time, built environment, and weather. We use a multigraph convolution network (GCN) to model the built environment, utilize a long short-term memory (LSTM) network to extract temporal features, and utilize a fully connected network (FCN) to model weather influence. We construct SGCNPM which can effectively fuse GCN, LSTM, and FCN, thus creating a prediction method considering the influence of multiple factors. The results of the real case in Tianjin, China, show that the proposed model can perform well in improving prediction accuracy. Also, we analyze the influence of factors on model prediction results in different periods.http://dx.doi.org/10.1155/2023/7407748 |
| spellingShingle | Yang Yang Xin Shao Yuting Zhu Enjian Yao Dongmei Liu Feng Zhao Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather Journal of Advanced Transportation |
| title | Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather |
| title_full | Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather |
| title_fullStr | Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather |
| title_full_unstemmed | Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather |
| title_short | Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather |
| title_sort | short term forecasting of dockless bike sharing demand with the built environment and weather |
| url | http://dx.doi.org/10.1155/2023/7407748 |
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