[1] XU Fulong, LI Zishen, ZHANG Kefei, et al. An investigation of optimal machine learning methods for the prediction of ROTI[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(2):1-15. [2] ZHANG Xiaohong, REN Xiaodong, WU Fengbo, et al. Short-term prediction of ionospheric TEC based on ARIMA model[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(1):9-16. [3] HAN Bing, ZHAO Guoze, WANG Lifeng, et al. Earthquake electromagnetic precursor anomalies detected by a new ground-based observation network[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(1):116-123. [4] 王培晓, 张恒才, 王海波, 等. ST-CFSFDP:快速搜索密度峰值的时空聚类算法[J]. 测绘学报, 2019, 48(11):1380-1390.DOI:10.11947/j.AGCS.2019.20180538. WANG Peixiao, ZHANG Hengcai, WANG Haibo, et al. Spatial-temporal clustering by fast search and find of density peaks[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11):1380-1390.DOI:10.11947/j.AGCS.2019.20180538. [5] 赵琨, 罗力, 杨凤芸. 利用北斗CORS反演大气可降水量的精度分析[J]. 测绘科学, 2021, 46(11):12-17. ZHAO Kun, LUO Li, YANG Fengyun. Accuracy analysis of precipitable water vapor retrieved by BeiDou CORS[J]. Science of Surveying and Mapping, 2021, 46(11):12-17. [6] 段功豪, 牛瑞卿, 赵艳南, 等. 基于动态指数平滑模型的降雨诱发型滑坡预测[J]. 武汉大学学报(信息科学版), 2016, 41(7):958-962. DUAN Gonghao, NIU Ruiqing, ZHAO Yannan, et al. Rainfall-induced landslide prediction based on dynamic exponential smoothing model[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7):958-962. [7] 冯宁, 郭晟楠, 宋超, 等. 面向交通流量预测的多组件时空图卷积网络[J]. 软件学报, 2019, 30(3):759-769. FENG Ning, GUO Shengnan, SONG Chao, et al. Multi-component spatial-temporal graph convolution networks for traffic flow forecasting[J]. Journal of Software, 2019, 30(3):759-769. [8] 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. [9] DEVRIES P M R, VIÉGAS F, WATTENBERG M, et al. Deep learning of aftershock patterns following large earthquakes[J]. Nature, 2018, 560(7720):632-634. [10] 张显峰, 崔伟宏. 集成GIS和细胞自动机模型进行地理时空过程模拟与预测的新方法[J]. 测绘学报, 2001, 30(2):148-155. ZHANG Xianfeng, CUI Weihong. Integrating GIS with cellular automaton model to establish a new approach for spatio-temporal process simulation and prediction[J]. Acta Geodaetica et Cartographic Sinica, 2001, 30(2):148-155. [11] 李静, 刘海砚, 郭文月, 等. 基于深度学习的人群活动流量时空预测模型[J]. 测绘学报, 2021, 50(4):522-531.DOI:10.11947/j.AGCS.2021.20200230. LI Jing, LIU Haiyan, GUO Wenyue, et al. A spatio-temporal network for human activity prediction based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(4):522-531.DOI:10.11947/j.AGCS.2021.20200230. [12] 吴华意, 黄蕊, 游兰, 等. 出租车轨迹数据挖掘进展[J]. 测绘学报, 2019, 48(11):1341-1356.DOI:10.11947/j.AGCS.2019.20190210. WU Huayi, HUANG Rui, YOU Lan, et al. Recent progress in taxi trajectory data mining[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11):1341-1356.DOI:10.11947/j.AGCS.2019.20190210. [13] YU Bin, SONG Xiaolin, GUAN Feng, et al. K-Nearest neighbor model for multiple-time-step prediction of short-term traffic condition[J]. Journal of Transportation Engineering, 2016, 142(6):04016018. [14] WU Shanhua, YANG Zhongzhen, ZHU Xiaocong, et al. Improved k-nn for short-term traffic forecasting using temporal and spatial information[J]. Journal of Transportation Engineering, 2014, 140(7):04014026. [15] ZHANG J, ZHENG Y, QI D. Deep spatio-temporal residual networks for citywide crowd flows prediction[C]//Proceedings of 2017 AAAI conference on artificial intelligence.San Francisco:AAAI,2017. [16] SHI X, GAO Z, LAUSEN L, ET AL. Deep learning for precipitation nowcasting:a benchmark and a new model[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Guangzhou:IEEE,2017:5622-5632. [17] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[C]//International Conference on Learning Representations.Toulon:ICLR, 2017. [18] ZHAO Ling, SONG Yujiao, ZHANG Chao, et al. T-GCN:a temporal graph convolutional network for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(9):3848-3858. [19] YU B, YIN H, ZHU Z. Spatio-temporal graph convolutional networks:a deep learning framework for traffic forecasting[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm:[s.n.], 2018:3634-3640. [20] ZHANG Yang, CHENG Tao, REN Yibin, et al. A novel residual graph convolution deep learning model for short-term network-based traffic forecasting[J]. International Journal of Geographical Information Science, 2020, 34(5):969-995. [21] 方孟元, 唐炉亮, 杨雪, 等. 基于低频GNSS轨迹的转向级城市交通信息精细预测[J]. 测绘学报, 2021, 50(11):1469-1477.DOI:10.11947/j.AGCS.2021.20210252. FANG Mengyuan, TANG Luliang, YANG Xue, et al. Fine-grained traffic information prediction at the turning-level based on low-frequency GNSS trajectory data[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(11):1469-1477.DOI:10.11947/j.AGCS.2021.20210252. [22] 李明晓, 张恒才, 仇培元, 等. 一种基于模糊长短期神经网络的移动对象轨迹预测算法[J]. 测绘学报, 2018, 47(12):1660-1669.DOI:10.11947/j.AGCS.2018.20170268. LI Mingxiao, ZHANG Hengcai, QIU Peiyuan, et al. Predicting future locations with deep fuzzy-LSTM network[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(12):1660-1669.DOI:10.11947/j.AGCS.2018.20170268. [23] 樊子德, 龚健雅, 刘博, 等. 顾及时空异质性的缺失数据时空插值方法[J]. 测绘学报, 2016, 45(4):458-465.DOI:10.11947/j.AGCS.2016.20150123. FAN Zide, GONG Jianya, LIU Bo, et al. A space-time interpolation method of missing data based on spatiotemporal heterogeneity[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4):458-465.DOI:10.11947/j.AGCS.2016.20150123. [24] 樊子德. 异质时空数据插值方法研究[J]. 测绘学报, 2017, 46(5):668. DOI:10.11947/j.AGCS.2017.20170003. FAN Zide. Spatio-temporal interpolation methods for heterogeneous spatio-temporal data[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(5):668.DOI:10.11947/j.AGCS.2017.20170003. [25] 程诗奋, 彭澎, 张恒才, 等. 异质稀疏分布时空数据插值、重构与预测方法探讨[J]. 武汉大学学报(信息科学版), 2020, 45(12):1919-1929. CHENG Shifen, PENG Peng, ZHANG Hengcai, et al. Review of interpolation, reconstruction and prediction methods for heterogeneous and sparsely distributed geospatial data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12):1919-1929. [26] YANG Jinming, PENG Zhongren, LIN Lei. Real-time spatiotemporal prediction and imputation of traffic status based on LSTM and Graph Laplacian regularized matrix factorization[J]. Transportation Research Part C:Emerging Technologies, 2021, 129:103228. [27] CHENG Shifen, LU Feng, PENG Peng, et al. Short-term traffic forecasting:an adaptive ST-KNN model that considers spatial heterogeneity[J]. Computers, Environment and Urban Systems, 2018, 71:186-198. DOI:10.1016/j.compenvurbsys.2018.05.009. [28] CHENG Shifen, LU Feng, PENG Peng, et al. Multi-task and multi-view learning based on particle swarm optimization for short-term traffic forecasting[J]. Knowledge-Based Systems, 2019, 180:116-132. [29] CAI Ling, JANOWICZ Krzysztof, MAI Gengchen, et al. Traffic transformer:capturing the continuity and periodicity of time series for traffic forecasting[J]. Transactions in GIS, 2020, 24(3):736-755. DOI:10.1111/tgis.12644. [30] YI Z, LIU X, MARKOVIC N, et al. Inferencing hourly traffic volume using data-driven machine learning and graph theory[J]. Computers, Environment and Urban Systems, 2021, 85:101548. DOI:10.1016/j.compenvurbsys.2020.101548. [31] CHEN Xinyu, SUN Lijun. Bayesian temporal factorization for multidimensional time series prediction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9):4659-4673. [32] YU H F, RAO N, DHILLON I S. Temporal regularized matrix factorization for high-dimensional time series prediction[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona:ACM Press, 2016:847-855. [33] CUI Zhiyong, LIN Longfei, PU Ziyuan, et al. Graph Markov network for traffic forecasting with missing data[J]. Transportation Research Part C:Emerging Technologies, 2020, 117:102671. DOI:10.1016/j.trc.2020.102671. [34] CHE Zhengping, PURUSHOTHAM Sanjay, CHO Kyunghyun, et al. Recurrent neural networks for multivariate time series with missing values[J]. Scientific Reports, 2018, 8(1):6085. DOI:10.1038/s41598-018-24271-9. [35] TIAN Yan, ZHANG Kaili, LI Jianyuan, et al. LSTM-Based traffic flow prediction with missing data[J]. Neurocomputing, 2018, 318:297-305. DOI:10.1016/j.neucom.2018.08.067. [36] BAI Shaojie, KOLTER J.Zizo, KOLTUN Vladlen. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[J/OL]. arXiv:1803.01271[cs], 2018[2021-05-05]. http://arxiv.org/abs/1803.01271. [37] YAN Jining, MU Lin, WANG Lizhe, et al. Temporal convolutional networks for the advance prediction of ENSO[J]. Scientific Reports, 2020, 10(1):8055. DOI:10.1038/s41598-020-65070-5. [38] MA Hao, CHEN Chao, ZHU Qing, et al. An ECG signal classification method based on dilated causal convolution[J]. Computational and Mathematical Methods in Medicine, 2021, 2021:6627939. [39] AYODEJI A, WANG Z, WANG W, et al. Causal augmented ConvNet:a temporal memory dilated convolution model for long-sequence time series prediction[J]. ISA transactions, 2022, 123:200-217. [40] ZHENG Y, YI X, LI M, et al. Forecasting fine-grained air quality based on big data[C]//Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining.Sydney:2015:[s.n.], 2267-2276. [41] HERSBACH H, BELL B, BERRISFORD P, et al. ERA5 hourly data on single levels from 1979 to present[J]. Copernicus climate change service (c3s) climate data store (cds), 2018, 10:24381.DOI:10.24381/cds.adbb2d47. [42] WANG Peixiao, ZHANG Tong, ZHENG Yueming, et al. A multi-view bidirectional spatiotemporal graph network for urban traffic flow imputation[J]. International Journal of Geographical Information Science, 2022, 36(6):1231-1257. [43] CHUNG J, GULCEHRE C, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[C]//Proceedings of 2014 Workshop on Deep Learning. Montreal:NIPS,2014. [44] 王培晓, 王海波, 傅梦颖, 等. 室内用户语义位置预测研究[J]. 地球信息科学学报, 2018, 20(12):1689-1698. WANG Peixiao, WANG Haibo, FU Mengying, et al. Research on semantic location prediction of indoor users[J]. Journal of Geo-Information Science, 2018, 20(12):1689-1698. [45] 沈乐君, 游志胜, 李晓峰. 自助重要性采样用于实时多目标视觉跟踪[J]. 自动化学报, 2012, 38(10):1663-1670. SHEN Lejun, YOU Zhisheng, LI Xiaofeng. Real-time visual tracking of multiple targets using bootstrap importance sampling[J]. Acta Automatica Sinica, 2012, 38(10):1663-1670. |