# A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning

arXiv: https://arxiv.org/abs/1803.02099 · HTML: https://mlsanity.com/paper/1803.02099/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 12 of 12)

- [Towards Explainable Traffic Flow Prediction with Large Language Models](https://mlsanity.com/paper/2404.02937/) (2024)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach](https://mlsanity.com/paper/2505.17637/) (2025)
- [Topology-Aware Conformal Prediction for Stream Networks](https://mlsanity.com/paper/2503.04981/) (2025)
- [Autonomous Deep Quality Monitoring in Streaming Environments](https://mlsanity.com/paper/2106.13955/) (2021)
- [Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies](https://mlsanity.com/paper/2111.02115/) (2021)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [Research on facial expression recognition based on Multimodal data fusion and neural network](https://mlsanity.com/paper/2109.12724/) (2021)
- [Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network](https://mlsanity.com/paper/2111.11337/) (2021)
- [Hiformer: Hybrid Frequency Feature Enhancement Inverted Transformer for Long-Term Wind Power Prediction](https://mlsanity.com/paper/2410.13303/) (2024)
- [Enhancing short-term traffic prediction by integrating trends and fluctuations with attention mechanism](https://mlsanity.com/paper/2504.19967/) (2025)
- [A comparative study of deep learning and ensemble learning to extend the horizon of traffic forecasting](https://mlsanity.com/paper/2504.21358/) (2025)

## Abstract (arXiv)

Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow forecasting, which can jointly and adaptively learn the spatial-temporal correlation features and long temporal interdependence of multi-modality traffic data by an attention auxiliary multimodal deep learning architecture. According to the highly nonlinear characteristics of multi-modality traffic data, the base module of our method consists of one-dimensional Convolutional Neural Networks (1D CNN) and Gated Recurrent Units (GRU) with the attention mechanism. The former is to capture the local trend features and the latter is to capture the long temporal dependencies. Then, we design a hybrid multimodal deep learning framework (HMDLF) for fusing share representation features of different modality traffic data by multiple CNN-GRU-Attention modules. The experimental results indicate that the proposed multimodal deep learning model is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness.