A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning

TL;DR

This paper introduces a hybrid multimodal deep learning approach combining CNN, GRU, and attention mechanisms to improve short-term traffic flow forecasting by capturing complex spatial-temporal features from multi-modality traffic data.

Contribution

It presents a novel hybrid framework that adaptively learns from multiple traffic data modalities using CNN, GRU, and attention, enhancing forecasting accuracy for nonlinear urban traffic flows.

Findings

Achieved high accuracy in short-term traffic flow prediction.

Effectively captures nonlinear spatial-temporal dependencies.

Demonstrated robustness across various traffic scenarios.

Abstract

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.