/Traffic-Prediction-Open-Code-Summary

Summary of open source code for deep learning models in the field of traffic prediction

MIT LicenseMIT

Deep learning models for traffic prediction

This is a summary for deep learning models with open code for traffic prediction.

These models are classified based on the following tasks.

  • Traffic flow prediction

  • Traffic speed prediction

  • On-Demand service prediction

  • Travel time prediction

  • Traffic accident prediction

  • Traffic Location Prediction

  • Others

Task Model Paper Code Publication
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
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
ASTGNN Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting Pytorch TKDE2021/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
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
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
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
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