[论文解读] A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning

Shengdong Du, Tianrui Li+2 more|arXiv (Cornell University)|Mar 6, 2018

Traffic Prediction and Management Techniques参考文献 31被引用 67

一句话总结

A hybrid multimodal deep learning framework using 1D CNNs, GRUs, and attention to jointly forecast short-term traffic flow by learning spatial-temporal correlations across multiple data modalities.

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.

研究动机与目标

• 为智能交通系统提供准确的短期交通流量预测提供动力。
• 解决城市交通数据中的非线性特征和多模态性。
• 学习模态内及跨模态的局部趋势特征和长期时序依赖。
• 开发一个可自适应融合来自不同模态的共享表示的框架。

提出的方法

• 基础模块将 1D CNNs用于局部趋势提取，与 GRUs 结合进行长期时序建模。
• 结合注意力机制，聚焦于有信息的时域特征。
• 设计一个混合多模态深度学习框架（HMDLF），以融合来自多模态数据的共享表示。
• 利用多个 CNN-GRU-Attention 模块来捕捉多样的模态特定模式。
• 提供一个跨模态的自适应学习架构用于多模态融合。

实验结果

研究问题

• RQ1 多模态深度学习方法是否能在短期交通流预测上优于单模态模型？
• RQ2 基于注意力的融合在交通预测中对跨模态信息的利用有多高效？
• RQ3 将 CNN 和 GRU 组件结合对捕捉交通数据中的局部与长期时序模式有何影响？
• RQ4 所提出的 HMDLF 框架能否稳健地处理城市交通的非线性特征？

主要发现

• 所提出的多模态深度学习模型在处理复杂非线性的城市交通流预测方面表现出色。
• 该架构通过带有注意力的 CNN-GRU 结构整合局部趋势和长期时序依赖。
• 通过 HMDLF 框架进行多模态融合在预测准确性和效果上具有令人满意的表现。