A reviewed paper list about deep learning methods for smart transportation systems
- STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting, IJCAI 2019, evaluated with BikeNYC and TaxiBJ datasets.
- Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction, CIKM 2019, evaluated with BikeNYC and TaxiBJ datasets.
- Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks, PAKDD 2019, evaluated with a private dataset about Didi.
- Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting, AAAI 2019, evaluated with two private datasets collected from Beijing and Shanghai by Didi.
- Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network, ICDE 2020, evaluated with two private datasets collected from Beijing and Shanghai by Didi.
- Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction, AAAI 2018, evaluated with a private dataset.
- Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction, AAAI 2019, evaluated with NYC-Taxi and NYC-Bike datasets.
- Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction, WWW 2019
- Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, ICLR 2018, evaluated with MetrLA and
- Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting, IJCAI 2018, evaluated with BJER4 and PeMS datasets.
- Graph WaveNet for Deep Spatial-Temporal Graph Modeling, IJCAI 2019, evaluated with MetrLA and PeMS-Bay dataset.
- Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning, KDD 2019, evaluated with MetrLA and TaxiBJ datasets.
- ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling, 2019, evaluated with MetrLA and PeMS datasets.