# T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks

## TL;DR

This paper introduces T-RippleGNN, a new model that improves traffic flow prediction using graph neural networks and ripple propagation, achieving better accuracy than existing methods.

## Contribution

The novel contribution is the integration of ripple propagation with attentive graph neural networks for more accurate traffic forecasting.

## Key findings

- T-RippleGNN reduces prediction errors by 2.24%-62.93% compared to state-of-the-art baselines.
- The model effectively captures spatial and temporal patterns in traffic flow data.

## Abstract

Recently, accurate traffic flow prediction has become a significant part of intelligent transportation systems, which can not only satisfy citizens’ travel need and life satisfaction, but also benefit urban traffic management and control. However, traffic forecasting remains highly challenging because of its complexity in both topology structure and time transformation. Inspired by the propagation idea of graph convolutional networks, we propose ripple-propagation-based attentive graph neural networks for traffic flow prediction (T-RippleGNN). Firstly, we adopt Ripple propagation to capture the topology structure of the traffic spatial model. Then, a GRU-based model is used to explore the traffic model through the timeline. Lastly, those two factors are combined and attention scores are assigned to differentiate their influences on the traffic flow prediction. Furthermore, we evaluate our approach with three real-world traffic datasets. The results show that our approach reduces the prediction errors by approximately 2.24%-62,93% compared with state-of-the-art baselines, and the effectiveness of T-RippleGNN in traffic forecasting is demonstrated.