# Enhanced Traffic Flow Prediction with Multi-Segment Fusion Tensor Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2408.04232 · HTML: https://mlsanity.com/paper/2408.04232/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 88 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2103.09504/) (2021)
- [NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models](https://mlsanity.com/paper/2007.00328/) (2020)
- [Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction](https://mlsanity.com/paper/2207.10830/) (2022)
- [Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation](https://mlsanity.com/paper/2304.07503/) (2023)
- [Tensor Graph Convolutional Network for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2401.07065/) (2024)
- [Dynamic Analysis and an Eigen Initializer for Recurrent Neural Networks](https://mlsanity.com/paper/2307.15679/) (2023)

## Abstract (arXiv)

Accurate traffic Flow Prediction can assist in traffic management, route planning, and congestion mitigation, which holds significant importance in enhancing the efficiency and reliability of intelligent transportation systems (ITS). However, existing traffic flow prediction models suffer from limitations in capturing the complex spatial-temporal dependencies within traffic networks. In order to address this issue, this study proposes a multi-segment fusion tensor graph convolutional network (MS-FTGCN) for traffic flow prediction with the following three-fold ideas: a) building a unified spatial-temporal graph convolutional framework based on Tensor M-product, which capture the spatial-temporal patterns simultaneously; b) incorporating hourly, daily, and weekly components to model multi temporal properties of traffic flows, respectively; c) fusing the outputs of the three components by attention mechanism to obtain the final traffic flow prediction results. The results of experiments conducted on two traffic flow datasets demonstrate that the proposed MS-FTGCN outperforms the state-of-the-art models.