[1] MEDINA-SALGADO B, SANCHEZ-DELACRUZ E, POZOS-PARRA P, et al. Urban traffic flow prediction techniques: A review[J]. Sustainable Computing:Informatics and Systems, 2022, 35: 100739 [2] GUO J, HUANG W, WILLIAMS B M. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification[J]. Transportation Research Part C:Emerging Technologies, 2014, 43: 50-64 [3] WILLIAMS B M, HOEL L A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results[J]. Journal of Transportation Engineering, 2003, 129(6): 664-72 [4] KUMAR S V, VANAJAKSHI L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data[J]. European Transport Research Review, 2015, 7(3): 1-9 [5] XIE Y, ZHANG Y, YE Z. Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition[J]. Computer Aided Civil and Infrastructure Engineering, 2007, 22(5): 326-34 [6] MODI S, BHATTACHARYA J, BASAK P. Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies[J]. Expert Systems with Applications, 2022, 189: 116140 [7] YU B, SONG X, GUAN F, 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 [8] YANG Y, LU H. Short-term traffic flow combined forecasting model based on SVM[C]//Proceedings of the 2010 International Conference on Computational and Information Sciences. US: IEEE, 2010: 262-265. [9] SHARMA B, KUMAR S, TIWARI P, et al. ANN based short-term traffic flow forecasting in undivided two lane highway[J]. Journal of Big Data, 2018, 5(1): 1-16 [10] YANG B, SUN S, LI J, et al. Traffic flow prediction using LSTM with feature enhancement[J]. Neurocomputing, 2019, 332: 320-7 [11] DAI G, MA C, XU X. Short-term traffic flow prediction method for urban road sections based on space– time analysis and GRU[J]. IEEE Access, 2019, 7: 143025-35 [12] CHEN C, LI K, TEO S G, et al. Exploiting spatio-temporal correlations with multiple 3d convolutional neural networks for citywide vehicle flow prediction[C]//Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM) . Singapore: IEEE, 2018: 893-898. [13] ZHENG Z, YANG Y, LIU J, et al. Deep and embedded learning approach for traffic flow prediction in urban informatics[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3927-3939 [14] CHENG X, ZHANG R, ZHOU J, et al. DeepTransport: Learning spatial-temporal dependency for traffic condition forecasting[C]//Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil: IEEE, 2018: 1-8. [15] DU S, LI T, GONG X, et al. A hybrid method for traffic flow forecasting using multimodal deep learning[J]. International Journal of Computational Intelligence Systems, 2020, 13(1): 85-97 [16] WANG H, ZHANG R, CHENG X, et al. Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 16137-16147 [17] 董成祥, 魏昕, 张坤鹏, 等. 基于图卷积网络的乘客打车需求预测[J]. 工业工程, 2022, 25(5): 98-105 DONG Chengxiang, WEI Xin, ZHANG Kunpeng, et al. Passenger ride-hailing demand prediction based on graph convolutional networksl[J]. Industrial Engineering Journal, 2022, 25(5): 98-105 [18] ZHAO L, SONG Y, ZHANG C, et al. T-gcn: A temporal graph convolutional network for traffic prediction[J]. IEEE transactions on Intelligent Transportation Systems, 2019, 21(9): 3848-3858 [19] WU Z, PAN S, LONG G, et al. Graph wavenet for deep spatial-temporal graph modeling [C]//IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence. Menlo Park, California: AAAI Press, 2019: 1907-1913 [20] LI Z, XIONG G, CHEN Y, et al. A hybrid deep learning approach with GCN and LSTM for traffic flow prediction[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC) . Auckland: IEEE, 2019: 1929-1933 [21] LI S, JIN X, XUAN Y, et al. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting [C/OL]//Proceeding of NeurIPS 2019. Vancouver, Canada: (2019-06-29) . https://doi.org/10.48550/arXiv.1907.00235. [22] REZA S, FERREIRA M C, MACHADO J, et al. A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks[J]. Expert Systems with Applications, 2022, 202: 117275 [23] DONG C, ZHANG K, WEI X, et al. Spatiotemporal graph attention network modeling for multi-step passenger demand prediction at multi-zone level[J]. Physica A:Statistical Mechanics and Its Applications, 2022, 603: 127789
|