Short-Term Traffic Forecasting
Research - Traffic Forecasting
A Novel Graph Convolutional Networks to Progressively Adapt to Online Data
In recent years, graph convolutional networks that possess the ability to adapt to the input data have shown promising results in several studies. In most cases, however, the adaptation has been made during the training phases of the models, which inevitably make the models vulnerable to unexpected traffic conditions such as road closure and traffic accident during the testing phases. In this study, we propose a novel traffic forecasting model, Progressive Graph Convolutional Network (PGCN) to make the model adapt to online traffic data. PGCN constructs a set of graphs by calculating learnable similarity measures among the node signals. The architecture of the model is based on Graph WaveNet. When applied to seven real-world datasets, PGCN consistently achieves state-of-the-art performance. This result shows that PGCN has the ability to generalize in different study areas by progressively adapting to online data.
SELECTED REFERENCES
-Y. Shin, Y. Yoon. (2024). PGCN: Progressive graph convolutional networks for spatial-temporal traffic forecasting. IEEE Transactions on Intelligent Transportation Systems
Performance Evaluation of Basic Elements of Deep Neural Network Models for Traffic Forecasting
Since 2014, various studies have proposed deep learning-based models to solve traffic forecasting problems. While earlier approaches have shown more wide range of implementations, the basic elements of the recent models can be categorized into a few groups - that are RNN, convolution, and self-attention for temporal feature extraction, and Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) for spatial feature extraction. However, there has been little effort to assess the characteristics of each element and make an in-depth evaluation. In this study, we thoroughly evaluated the performance and characteristics of basic elements of traffic forecasting models through extensive and multi-faceted experiments on four real-world datasets. The result reveals that there is no single element that is superior to the others in all aspects. Interesting outcomes include that the convolution-based models show more accurate overall performance than the attention-based models, while the attention-based models show more robustness against abnormal conditions.
SELECTED REFERENCES
- Y. Shin, Y. Yoon*, "A Comparative Study on Basic Elements of Deep Learning Models for Spatial-Temporal Traffic Forecasting", AAAI-22 workshop: AI for Transportation, 2022.
Short-Term Traffic Forecasting Using Graph Convolutional Networks [LINK]
Traffic forecasting problem is a research area in transportation engineering that has flourished over the last couple of decades, and started to garner broader research interest as a key technical enabler of the adaptive traffic management. The recent surge of Graph Convolutional Networks has led to acute improvement on the performance of traffic forecasting tasks. However, many studies overlook the features that can represent the transportation networks such as speed limit, distance, and flow direction. In our research, we propose Multi-Weight Traffic Graph Convolutional Networks (MW-TGC) to incorporate aforementioned features in a single model and to reflect more spatial dynamicity in traffic forecasting problem. Experiment on two real-world datasets show that the proposed model outperforms the state-of-the-art models.
SELECTED REFERENCES
- Y. Shin and Y. Yoon, "Incorporating Dynamicity of Transportation Network With Multi-Weight Traffic Graph Convolutional Network for Traffic Forecasting," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2020.3031331.
