Short-Term Traffic Forecasting by Mining the Non-Stationarity of Spatiotemporal Patterns
文献类型：期刊论文
| 作者 | Cheng, Shifen2,3; Lu, Feng1,2,3,4; Peng, Peng2,3 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2021-10-01 |
| 卷号 | 22期号:10页码:6365-6383 |
| 关键词 | Roads Predictive models Spatiotemporal phenomena Adaptation models Forecasting Vehicle dynamics Partitioning algorithms Dynamic spatiotemporal k-nearest neighbor model short-term traffic forecasting spatiotemporal pattern temporal non-stationarity |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2020.2991781 |
| 通讯作者 | Lu, Feng(luf@lreis.ac.cn) |
| 英文摘要 | Short-term traffic forecasting is important for the development of an intelligent traffic management system. Critical to the performance of the traffic prediction model utilized in such a system is accurate representation of the spatiotemporal traffic characteristics. This can be achieved by integrating spatiotemporal traffic information or the dynamic traffic characteristics in the modeling process. The currently employed spatiotemporal k-nearest neighbor (STKNN) model is based on the spatial heterogeneity and adaptive spatiotemporal parameters of the traffic to improve the prediction accuracy. However, the non-stationary characteristics of the traffic cannot be fully represented by simply modeling the entire time range or all the time partitions based on experience. We therefore developed a dynamic STKNN model (D-STKNN) for short-term traffic forecasting based on the non-stationary spatiotemporal pattern of the road traffic. The different traffic patterns along the road are first automatically determined using an affinity propagation clustering algorithm. The Warped K-Means algorithm is then used to automatically partition the time periods for each traffic pattern. Finally, the D-STKNN model is developed based on the three-dimensional spatiotemporal tensor data models for the different road segments with different traffic patterns during different time periods. The D-STKNN model was verified through extensive experiments performed using actual vehicular speed datasets collected from city roads in Beijing, China, and expressways in California, U.S.A. The proposed model outperforms existing seven baselines in different time periods under different traffic patterns. The results confirmed the imperative of considering the non-stationary spatiotemporal traffic pattern in developing a model for short-term traffic prediction. |
| WOS关键词 | PREDICTION ; VOLUME ; MODEL |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23010202] ; Regional Key Project through the Science and Technology Service Network Initiative of Chinese Academy of Sciences[KFJ-STS-QYZD-xxx] ; China Postdoctoral Science Foundation[2019M660774] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000704117000026 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Strategic Priority Research Program of the Chinese Academy of Sciences ; Regional Key Project through the Science and Technology Service Network Initiative of Chinese Academy of Sciences ; China Postdoctoral Science Foundation |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/167046]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lu, Feng |
| 作者单位 | 1.Fuzhou Univ, Acad Digital China, Fuzhou 350003, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cheng, Shifen,Lu, Feng,Peng, Peng. Short-Term Traffic Forecasting by Mining the Non-Stationarity of Spatiotemporal Patterns[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021,22(10):6365-6383. |
| APA | Cheng, Shifen,Lu, Feng,&Peng, Peng.(2021).Short-Term Traffic Forecasting by Mining the Non-Stationarity of Spatiotemporal Patterns.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,22(10),6365-6383. |
| MLA | Cheng, Shifen,et al."Short-Term Traffic Forecasting by Mining the Non-Stationarity of Spatiotemporal Patterns".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22.10(2021):6365-6383. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。