/Deep_Learning_Smart_Transportation

A reviewed paper list about applying deep learning models for smarter transportation systems

Deep Learning for Smart Transportation Systems

A reviewed paper list about deep learning methods for smart transportation systems

Passenger Demand Prediction

  1. STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting, IJCAI 2019, evaluated with BikeNYC and TaxiBJ datasets.
  2. Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction, CIKM 2019, evaluated with BikeNYC and TaxiBJ datasets.
  3. Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks, PAKDD 2019, evaluated with a private dataset about Didi.
  4. Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting, AAAI 2019, evaluated with two private datasets collected from Beijing and Shanghai by Didi.
  5. Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network, ICDE 2020, evaluated with two private datasets collected from Beijing and Shanghai by Didi.
  6. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction, AAAI 2018, evaluated with a private dataset.
  7. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction, AAAI 2019, evaluated with NYC-Taxi and NYC-Bike datasets.
  8. Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction, WWW 2019

Traffic Speed/Flow Prediction

  1. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, ICLR 2018, evaluated with MetrLA and
  2. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting, IJCAI 2018, evaluated with BJER4 and PeMS datasets.
  3. Graph WaveNet for Deep Spatial-Temporal Graph Modeling, IJCAI 2019, evaluated with MetrLA and PeMS-Bay dataset.
  4. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning, KDD 2019, evaluated with MetrLA and TaxiBJ datasets.
  5. ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling, 2019, evaluated with MetrLA and PeMS datasets.

Travel Time Estimation

Route Planning

Bike/Taxi Schedual