Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

By Hongfei Wang, Bowen Du, Z. Bhuiyan, Hongyuan Ma, Jianwei Liu, Lihong Wang, Zeyu Yang, Linfeng Du, Senzhang Wang, Philip S. Yu. Information Sciences, 2020

315 citations recorded by the source corpus.

Abstract Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as subway/bus stations, is a practical application and of great significance for urban traffic planning, control, guidance, etc. Recently deep learning based methods are promised to learn the spatial-temporal features from high non-linearity and complexity of traffic flows. However, it is still very challenging to handle so much complex factors including the urban transportation network topological structures and the laws of traffic flows with spatial and temporal dependencies. Considering both the static hybrid urban transportation network structures and dynamic spatial-temporal relationships among stations from historical traffic passenger flows, a more effective and fine-grained spatial-temporal features learning framework is necessary. In this paper, we propose a novel spatial-temporal incidence dynamic graph neural networks framework for urban traffic passenger flows prediction. We first model dynamic traffic station relationships over time as spatial-temporal incidence dynamic graph structures based on historically traffic passenger flows. Then we design a novel dynamic graph recurrent convolutional neural network, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures and transportation hubs. To fully utilize the historical passenger flows, we sample the short-term, medium-term and long-term historical traffic data in training, which can capture the periodicity and trend of the traffic passenger flows at different stations. We conduct extensive experiments on different types of traffic passenger flows datasets including subway, taxi and bus flows in Beijing. The results show that the proposed Dynamic-GRCNN effectively captures comprehensive spatial-temporal correlations significantly and outperforms both traditional and deep learning based urban traffic passenger flows prediction methods.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/72a20f574ebc22b3267aff45b6161d7d79625945

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

  1. Jiayun Luo
  2. Weiwei Jiang
  3. Nizar Bouguila
  4. Saeed Rahmani
  5. Asiye Baghbani
  6. Zachary Patterson

In Transportation

  1. Jiayun Luo
  2. Weiwei Jiang
  3. Nizar Bouguila
  4. Saeed Rahmani
  5. Asiye Baghbani
  6. Zachary Patterson

Citations in the corpus