| Task | Traditional | CNN-based | RNN-based | GCN-based | Attention-based |
|---|---|---|---|---|---|
| Traffic flow prediction | AutoEncoder [1] | ST-ResNet [2], ACFM [3], STDN [4] | FC-RNN [5], Seq2Seq [6] | AGCRN [7], CONVGCN [8], STSGCN [9], ToGCN [10], Multi-STGCnet [11] | ASTGCN [12], ResLSTM [13], CRANN [14], DGCN [15], DSAN [16] |
| Traffic speed prediction | AutoEncoder [1] | — | FC-RNN [5], Seq2Seq [6] | DCRNN [17], STGCN [18], GWNET [19], MTGNN [20], TGCN [21], TGCLSTM [22], ATDM [23], GTS [24] | GMAN [25], STAGGCN [26], HGCN [27], ST-MGAT [28] |
| On-Demand service prediction | AutoEncoder [1] | DMVSTNet [29] | FC-RNN [5], Seq2Seq [6] | CCRNN [30] | STG2Seq [31] |
| Trajectory next-location prediction | FPMC [32] | — | RNN [33], ST-RNN [34], ATST-LSTM [35], SERM [36], DeepMove [37], HST-LSTM [38], LSTPM [39], CARA [40] | — | GeoSAN [41], STAN [42] |
[1] This baseline model is implemented by ourselves for traffic state prediction task based on AutoEncoder.
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[4] Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2019. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. In AAAI. AAAI Press, 5668–5675.
[5] This baseline model is implemented by ourselves for traffic state prediction task based on RNN.
[6] This baseline model is implemented by ourselves for traffic state prediction task based on Encoder-Decoder structure.
[7] Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In NeurIPS.
[8] Jinlei Zhang, Feng Chen, Yinan Guo, and Xiaohong Li. 2020. Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit. IET Intell. Transp. Syst.14, 10 (2020), 1210–1217.
[9] Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan. 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. In AAAI. AAAI Press, 914–921.
[10] Han Qiu, Qinkai Zheng, Mounira Msahli, Gerard Memmi, Meikang Qiu, and Jialiang Lu. 2020. Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction. IEEE Trans. Intell. Transp. Syst.(2020).
[11] Jiexia Ye, Juanjuan Zhao, Kejiang Ye, and Chengzhong Xu. 2020. Multi-STGCnet: A Graph Convolution Based Spatial-Temporal Framework for Subway Passenger Flow Forecasting. In IJCNN. IEEE, 1–8.
[12] Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. In AAAI. AAAI Press, 922–929.
[13] Jinlei Zhang, Feng Chen, Zhiyong Cui, Yinan Guo, and Yadi Zhu. 2020. Deep learning architecture for short-term passenger flow forecasting in urban rail transit. IEEE Trans. Intell. Transp. Syst.(2020).
[14] Rodrigo de Medrano and José Luis Aznarte. 2020. A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction. Appl. Soft Comput.96 (2020), 1066
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[16] Haoxing Lin, Rufan Bai, Weijia Jia, Xinyu Yang, and Yongjian You. 2020. Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction. In KDD. ACM, 36–46.
[17] Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In ICLR (Poster). OpenReview.net.
[18] Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In IJCAI. ijcai.org, 3634–3640.
[19] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. In IJCAI. ijcai.org, 1907–1913.
[20] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In KDD. ACM, 753–763.
[21] Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. 2020. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Trans. Intell. Transp. Syst. 21, 9 (2020), 3848–385.
[22] Zhiyong Cui, Kristian Henrickson, Ruimin Ke, and Yinhai Wang. 2020. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Frame work for Network-Scale Traffic Learning and Forecasting. IEEE Trans. Intell. Transp. Syst. 21, 11 (2020), 4883–4894.
[23] Rodrigo de Medrano and José Luis Aznarte. 2020. On the Inclusion of Spatial Information for Spatio-Temporal Neural Networks. CoRR abs/2007.07559 (2020).
[24] Chao Shang, Jie Chen, and Jinbo Bi. 2021. Discrete Graph Structure Learning for Forecasting Multiple Time Series. CoRR abs/2101.06861 (2021).
[25] Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020. GMAN: A Graph Multi-Attention Network for Traffic Prediction. In AAAI. AAAI Press,1234–1241.
[26] Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, and Haisong Zhang. 2020. Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting. In CIKM. ACM, 1025–1034.
[27] Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, and Baocai Yin. 2021. Hierarchical Graph Convolution Networks for Traffic Forecasting. In AAAI. AAAI Press,151-159.
[28] Kelang Tian, Jingjie Guo, Kejiang Ye, and Cheng-Zhong Xu. 2020. ST-MGAT: Spatial-Temporal Multi-Head Graph Attention Networks for Traffic Forecasting. In ICTAI. IEEE, 714–721.
[29] Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. In AAAI. AAAI Press, 2588.
[30] Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, and Hui Xiong. 2021. Coupled Layer-wise Graph Convolution for Transportation Demand Prediction. In AAAI. AAAI Press, 4617–46.
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[32] Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In WWW. ACM, 811–820.
[33] This baseline model is implemented by ourselves for trajectory next-location prediction task based on RNN.
[34] Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts. In AAAI. AAAI Press, 194–200.
[35] Liwei Huang, Yutao Ma, Shibo Wang, and Yanbo Liu. 2019. An attention-based spatiotemporal lstm network for next poi recommendation. IEEE Transactions on Services Computing(2019).
[36] Di Yao, Chao Zhang, Jian-Hui Huang, and Jingping Bi. 2017. SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories. In CIKM. ACM, 2411–24.
[37] Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. DeepMove: Predicting Human Mobility with Attentional Recurrent Networks. In WWW. ACM, 1459–1468.
[38] Dejiang Kong and Fei Wu. 2018. HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction. In IJCAI. ijcai.org, 2341–2347.
[39] Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, and HongzhiYin. 2020. Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation. In AAAI. AAAI Press, 214-221.
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[41] Defu Lian, Yongji Wu, Yong Ge, Xing Xie, and Enhong Chen. 2020. Geography-Aware Sequential Location Recommendation. In KDD. ACM, 2009–2019.
[42] Yingtao Luo, Qiang Liu, and Zhaocheng Liu. 2021. STAN: Spatio-Temporal Attention Network for Next Location Recommendation. CoRR abs/2102.04095 (2021).