| Traffic flow prediction | ST-ResNet | Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction | tf，Pytorch，Keras | AAAI2017/A |
| ACFM | ACFM: A Dynamic Spatial-Temporal Network for Traffic Prediction | Pytorch | ACM MM2018/A |
| STDN | Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction | Keras | AAAI2019/A |
| ASTGCN | Attention based spatial-temporal graph convolutional networks for traffic flow forecasting | Pytorch | AAAI2019/A |
| ST-MetaNet | Urban traffic prediction from spatio-temporal data using deep meta learning | MXNet | KDD2019/A |
| STSGCN | Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting | MXNet | AAAI2020/A |
| STGNN | STGNN: Traffic Flow Prediction via Spatial Temporal Graph Neural Network | Pytorch | WWW2020/A |
| AGCRN | Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting | Pytorch | NIPS2020/A |
| DSAN | Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction | tf2 | KDD2020/A |
| MPGCN | Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network | Pytorch | ICDE2020/A |
| ST-GDN | Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network | tf | AAAI2021/A |
| TrGNN | Traffic Flow Prediction with Vehicle Trajectories | Pytorch | AAAI2021/A |
| STFGNN | Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting | MXNet | AAAI2021/A |
| STGODE | STGODE : Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting | Pytorch | KDD2021/A |
| ASTGNN | Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting | Pytorch | TKDE2021/A |
| STG-NCDE | Graph Neural Controlled Differential Equations for Traffic Forecasting | Pytorch | AAAI2022/A |
| STDEN | STDEN Towards Physics-Guided Neural Networks for Traffic Flow Prediction | Pytorch | AAAI2022/A |
| SAE | Traffic Flow Prediction With Big Data: A Deep Learning Approach | Keras | TITS2015/B |
| STNN | Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery | Pytorch | ICDM2017/B |
| ST-3DNet | Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting | Keras | TITS2019/B |
| STAG-GCN | Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting | Pytorch | CIKM2020/B |
| ST-CGA | Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting | Keras | CIKM2020/B |
| ResLSTM | Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit | Keras | TITS2020/B |
| DGCN | Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation | Pytorch | TITS2020/B |
| ToGCN | Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction | Pytorch | TITS2020/B |
| Multi-STGCnet | Multi-STGCnet: A Graph Convolution Based Spatial-Temporal Framework for Subway Passenger Flow Forecasting | Keras | IJCNN2020/C |
| Conv-GCN | Multi-Graph Convolutional Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit | Keras | IET-ITS2020/C |
| TCC-LSTM-LSM | A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting | Keras | Neurocomputing2021/C |
| LSTM/GRU | Using LSTM and GRU neural network methods for traffic flow prediction | Keras | YAC2016/none |
| Cluster_LSTM | Foreseeing Congestion using LSTM on Urban Traffic Flow Clusters | Keras | ICSAI2019/none |
| CRANN | A Spatio-Temporal Spot-Forecasting Framework forUrban Traffic Prediction | Pytorch | Applied Soft Computing2020/none |
| GNN-flow | Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks | Pytorch | IEEE SMARTCOMP2020/none |
| Deep_Sedanion_Network | Traffic flow prediction using Deep Sedenion Networks | Pytorch | arXiv2020 |
| MATGCN | Multi-Attention Temporal Graph Convolution Network for Traffic Flow Forecasting | Pytorch | 本科毕设 |
| Traffic speed prediction | DCRNN | Diffusion convolutional recurrent neural network: Data-driven traffic forecasting | tf，Pytorch | ICLR2018/none |
| STGCN | Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting | tf，MXNet，Pytorch，Keras | IJCAI2018/A |
| BaiduTraffic | Deep sequence learning with auxiliary information for traffic prediction | tf | KDD2018/A |
| Graph WaveNet | Graph wavenet for deep spatial-temporal graph modeling | Pytorch | IJCAI2019/A |
| Graph WaveNet-V2 | Incrementally Improving Graph WaveNet Performance on Traffic Prediction | Pytorch | arXiv2019/none |
| GMAN | Gman: A graph multi-attention network for traffic prediction | tf | AAAI2020/A |
| MRA-BGCN | Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting | Pytorch | AAAI2020/A |
| MTGNN | Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks | Pytorch | KDD2020/A |
| Curb-GAN | Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks | Pytorch | KDD2020/A |
| AF | Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks | tf | ICDE2020/A |
| FC-GAGA | FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting | tf | AAAI2021/A |
| HGCN | Hierarchical Graph Convolution Networks for Traffic Forecasting | Pytorch | AAAI2021/A |
| ST-Norm | ST-Norm: Spatial and Temporal Normalization for Multi-variateTime Series Forecasting | Pytorch | KDD2021/A |
| DMSTGCN | Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting | Pytorch | KDD2021/A |
| GTS | Discrete Graph Structure Learning for Forecasting Multiple Time Series | Pytorch | ICLR2021/none |
| DKFN | Graph Convolutional Networks with Kalman Filtering for Traffic Prediction | Pytorch | SIGSPATIAL2020/none |
| T-GCN | T-gcn: A temporal graph convolutional network for traffic prediction | tf | TITS2019/B |
| TGC-LSTM | Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting | Pytorch | TITS2020/B |
| ST-GRAT | ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed | Pytorch | CIKM2020/B |
| GaAN | GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs | MXNet | UAI2018/B |
| TL-DCRNN | Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting | tf | ICPR2020/C |
| ST-MGAT | ST-MGAT: Spatial-Temporal Multi-Head Graph Attention Networks for Traffic Forecasting | Pytorch | ICTAI2020/C |
| DGFN | Dynamic Graph Filters Networks: A Gray-box Model for Multistep Traffic Forecasting | tf2 | ITSC2020/none |
| ATDM | On the Inclusion of Spatial Information for Spatio-Temporal Neural Networks | Pytorch | arXiv2020/none |
| STTN | Spatial-Temporal Transformer Networks for Traffic Flow Forecasting | Pytorch | arXiv2020/none |
| DGCRN | Dynamic Graph Convolutional Recurrent Network for Traffic Prediction Benchmark and Solution | Pytorch | arXiv2021/none |
| STAWnet | Spatial-temporal attention wavenet: A deep learning framework for traffic prediction considering spatial-temporal dependencies | Pytorch | IET Intelligent Transport Systems2021/C |
| On-Demand service prediction | DMVST-Net | Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction | Keras | AAAI2018/A |
| STG2Seq | Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting | tf | IJCAL2019/A |
| GEML | Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling | Keras | KDD2019/A |
| CCRNN | Coupled Layer-wise Graph Convolution for Transportation Demand Prediction | Pytorch | AAAI2021/A |
| CSTN | Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction | Keras | TITS2019/B |
| GraphLSTM | Grids versus graphs: Partitioning space for improved taxi demand-supply forecasts | Pytorch | TITS2020/B |
| DPFE | Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data | Pytorch | Transportation Research Part C: Emerging Technologies2018/none |
| ST-ED-RMGC | Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network | Keras | Transportation Research Part C: Emerging Technologies2021/none |
| Travel time prediction | DeepTTE | When will you arrive? estimating travel time based on deep neural networks | Pytorch | AAAI2018/A |
| HetETA | HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival | tf | KDD2020/A |
| TTPNet | TTPNet: A Neural Network for Travel Time Prediction Based on Tensor Decomposition and Graph Embedding | Pytorch | TKDE2020/A |
| HyperETA | HyperETA: An Estimated Time of Arrival Method based on Hypercube Clustering | Pytorch | techrxiv2021/None |
| GSTA | GSTA: gated spatial–temporal attention approach for travel time prediction | tf2 | Neural Computing and Applications2021/None |
| Traffic accident prediction | RiskOracle | RiskOracle: A Minute-Level Citywide Traffic Accident Forecasting Framework | tf | AAAI2020/A |
| RiskSeq | Foresee Urban Sparse Traffic Accidents: A Spatiotemporal Multi-Granularity Perspective | tf | TKDE2020/A |
| GSNet | GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting | Pytorch | AAAI2021/A |
| DSTGCN | Deep Spatio-Temporal Graph Convolutional Network for Traffic Accident Prediction | Pytorch | Neurocomputing2020/C |
| Traffic location prediction | STRNN | Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts | Pytorch | AAAI2016/A |
| DeepMove | DeepMove: Predicting Human Mobility with Attentional Recurrent Networks | Pytorch | WWW2018/A |
| HST-LSTM | HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction | Pytorch | IJCAI2018/A |
| VANext | Predciting Human Mobility via Variational Attention | tf | WWW2019/A |
| FQA | Multi-agent Trajectory Prediction with Fuzzy Query Attention | Pytorch | NIPS2020/A |
| MALMCS | Dynamic Public Resource Allocation based on Human Mobility Prediction | python | UbiComp2020/A |
| SERM | SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories | Keras | CIKM2017/B |
| Map matching | ST-Matching | Map-matching for low-sampling-rate GPS trajectories | Python | SIGSPATIAL2009/None |
| IVMM | An Interactive-Voting Based Map Matching Algorithm | Python | MDM2010/C |
| HMMM | Hidden Markov map matching through noise and sparseness | Python | SIGSPATIAL2009/None |
| PIF | The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data | Python | TITS2014/B |
| Others | seq2seq | Sequence to Sequence Learning with Neural Networks | Keras | NIPS2014/A |
| NASR | Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation | tf | KDD2019/A |
| HRNR | Learning Effective Road Network Representation with Hierarchical Graph Neural Networks | Pytorch | KDD2020/A |
| SHARE | Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction | Pytorch | AAAI2020/A |
| TALE | Pre-training Time-Aware Location Embeddings from Spatial-Temporal Trajectories | Pytorch | TKDE2021/A |
| PVCGN | Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction | Pytorch | TITS2020/B |
| DCRNN | Evaluation and prediction of transportation resilience under extreme weather events: A diffusion graph convolutional approach | tf | Transportation Research Part C: Emerging Technologies2020/none |
| LibCity | LibCity: An Open Library for Traffic Prediction | Pytorch | SIGSPATIAL2021/None |