GLSNN Network: A Multi-Scale Spatiotemporal Prediction Model for Urban Traffic Flow
文献类型：期刊论文
| 作者 | Cai, Benhe2; Wang, Yanhui2; Huang, Chong1; Liu, Jiahao2; Teng, Wenxin2 |
| 刊名 | SENSORS
 |
| 出版日期 | 2022-11-01 |
| 卷号 | 22期号:22页码:14 |
| 关键词 | GLSNN model spatiotemporal data fusion multi-scale traffic flow prediction spatiotemporal dependence |
| DOI | 10.3390/s22228880 |
| 通讯作者 | Huang, Chong(huangch@lreis.ac.cn) |
| 英文摘要 | Traffic flow prediction is a key issue in intelligent transportation systems. The growing trend in data disclosure has created more potential sources for the input for predictive models, posing new challenges to the prediction of traffic flow in the era of big data. In this study, the prediction of urban traffic flow was regarded as a spatiotemporal prediction problem, focusing on the traffic speed. A Graph LSTM (Long Short-Term Memory) Spatiotemporal Neural Network (GLSNN) model was constructed to perform a multi-scale spatiotemporal fusion prediction based on the multi-source input data. The GLSNN model consists of three parts: MS-LSTM, LZ-GCN, and LSTM-GRU. We used the MS-LSTM module to scale the traffic timing data, and then used the LZ-GCN network and the LSTM-GRU network to capture both the time and space dependencies. The model was tested on a real traffic dataset, and the experiment results verified the superior performance of the GLSNN model on both a high-precision and multi-scale prediction of urban traffic flow. |
| WOS关键词 | SPEED PREDICTION |
| 资助项目 | National Natural Science Foundation of China[42171224] ; Great Wall Scholars Program[CIT TCD20190328] ; CAS Earth Big Data Science Project[XDA19060302] ; Key Research Projects of National Statistical Science of China[2018LZ27] |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000887842600001 |
| 资助机构 | National Natural Science Foundation of China ; Great Wall Scholars Program ; CAS Earth Big Data Science Project ; Key Research Projects of National Statistical Science of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187348]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Huang, Chong |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Capital Normal Univ, Key Lab 3 Dimens Informat Acquisit & Applicat, Minist Educ, Beijing 100048, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cai, Benhe,Wang, Yanhui,Huang, Chong,et al. GLSNN Network: A Multi-Scale Spatiotemporal Prediction Model for Urban Traffic Flow[J]. SENSORS,2022,22(22):14. |
| APA | Cai, Benhe,Wang, Yanhui,Huang, Chong,Liu, Jiahao,&Teng, Wenxin.(2022).GLSNN Network: A Multi-Scale Spatiotemporal Prediction Model for Urban Traffic Flow.SENSORS,22(22),14. |
| MLA | Cai, Benhe,et al."GLSNN Network: A Multi-Scale Spatiotemporal Prediction Model for Urban Traffic Flow".SENSORS 22.22(2022):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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