# Spatial linear transformer and temporal convolution network for traffic flow prediction

## TL;DR

This paper introduces a new model for predicting traffic flow by combining spatial and temporal features to improve accuracy and efficiency.

## Contribution

The novel contribution is the SLTTCN model, which integrates a spatial linear transformer with a bidirectional temporal convolution network for traffic flow prediction.

## Key findings

- SLTTCN outperforms other models in traffic flow prediction across multiple error metrics.
- The spatial linear transformer effectively captures dynamic global spatial dependencies with reduced computational complexity.
- Attention visualization confirms the model's ability to capture meaningful spatial relationships.

## Abstract

Accurately obtaining accurate information about the future traffic flow of all roads in the transportation network is essential for traffic management and control applications. In order to address the challenges of acquiring dynamic global spatial correlations between transportation links and modeling time dependencies in multi-step prediction, we propose a spatial linear transformer and temporal convolution network (SLTTCN). The model is using spatial linear transformers to aggregate the spatial information of the traffic flow, and bidirectional temporal convolution network to capture the temporal dependency of the traffic flow. The spatial linear transformer effectively reduces the complexity of data calculation and storage while capturing spatial dependence, and the time convolutional network with bidirectional and gate fusion mechanisms avoids the problems of gradient vanishing and high computational cost caused by long time intervals during model training. We conducted extensive experiments using two publicly available large-scale traffic data sets and compared SLTTCN with other baselines. Numerical results show that SLTTCN achieves the best predictive performance in various error measurements. We also performed attention visualization analysis on the spatial linear transformer, verifying its effectiveness in capturing dynamic global spatial dependency.