Related Work
Dynamic Graph
- Predicting Path Failures in Time evolving graphs [data+code(TensorFlow)] [KDD ’19]
- Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting [AAAI ’19]
Traffic Prediction
- GMAN: A Graph Multi-Attention Network for Traffic Prediction [AAAI ’20]
- Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning [data+code(MXNet)] [KDD ’19]
- Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting [code(PyTorch)] [ IEEE Transactions on Intelligent Transportation Systems ’19]
- T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction [data+code(TensorFlow)] [ IEEE Transactions on Intelligent Transportation Systems ’19]
- Speed Prediction in Large and Dynamic Traffic Sensor Networks [Information Systems ’19]
- Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends [ IEEE Transactions on Intelligent Transportation Systems ’19]
- Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting [data+code( MXNet)] [AAAI ’19]
- TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction [SIGSPATIAL ’19]
- Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting [data+code(TensorFlow)] [IJCAI ’18]
- Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting [data+code(TensorFlow)] [ICLR ’18]
Approaches Dealing with Missing Data
- BRITS: Bidirectional Recurrent Imputation for TimeSeries [data+code(PyTorch)][NerIPS ’18]
- LSTM-based traffic flow prediction with missing data [Neurocomputing ’18]
Spatial Info Integration
- Graph Convolutional Networks for Road Networks [SIGSPATIAL ’19]