How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

Abstract

In recent years, various deep learning architectures have been proposed to solve complex challenges (e.g. spatial dependency, temporal dependency) in traffic domain, which have achieved satisfactory performance. These architectures are composed of multiple deep learning techniques in order to tackle various challenges in traffic tasks. Traditionally, convolution neural networks (CNNs) are utilized to model spatial dependency by decomposing the traffic network as grids. However, many traffic networks are graph-structured in nature. In order to utilize such spatial information fully, it's more appropriate to formulate traffic networks as graphs mathematically. Recently, various novel deep learning techniques have been developed to process graph data, called graph neural networks (GNNs). More and more works combine GNNs with other deep learning techniques to construct an architecture dealing with various challenges in a complex traffic task, where GNNs are responsible for extracting spatial correlations in traffic network. These graph-based architectures have achieved state-of-the-art performance. To provide a comprehensive and clear picture of such emerging trend, this survey carefully examines various graph-based deep learning architectures in many traffic applications. We first give guidelines to formulate a traffic problem based on graph and construct graphs from various kinds of traffic datasets. Then we decompose these graph-based architectures to discuss their shared deep learning techniques, clarifying the utilization of each technique in traffic tasks. What's more, we summarize some common traffic challenges and the corresponding graph-based deep learning solutions to each challenge. Finally, we provide benchmark datasets, open source codes and future research directions in this rapidly growing field.

arXiv comment21pages, 11figures

References in corpus (39)

• Sequence to Sequence Learning with Neural Networks
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• A Comprehensive Survey on Graph Neural Networks
• Generative Adversarial Networks: An Overview
• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Geometric deep learning: going beyond Euclidean data
• Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
• WaveNet: A Generative Model for Raw Audio
• T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
• Graph Neural Networks: A Review of Methods and Applications
• Deep Convolutional Networks on Graph-Structured Data
• Recent Advances in Recurrent Neural Networks
• Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach
• Learning Deep Generative Models of Graphs
• Neural Machine Translation in Linear Time
• Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
• Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach
• Recurrent Neural Networks (RNNs): A gentle Introduction and Overview
• Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks
• Deep Deterministic Policy Gradient for Urban Traffic Light Control
• EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
• A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation
• Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting
• Semi-Implicit Graph Variational Auto-Encoders
• Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network
• ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling
• 3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting
• Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
• Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction
• Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
• GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction
• "How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction
• Revisiting Flow Information for Traffic Prediction
• Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview
• Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction

Cited by in corpus (14)

• Graph Neural Network for Traffic Forecasting: A Survey
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
• Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions
• Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
• Graph Mining for Cybersecurity: A Survey
• Graph Deep Learning for Time Series Forecasting
• Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction
• Uncertainty-aware Traffic Prediction under Missing Data
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
• Probabilistic Traffic Forecasting with Dynamic Regression
• An Overview on the Application of Graph Neural Networks in Wireless Networks
• Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction
• Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting
• Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction