Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions

Abstract

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been devoted to this area, especially deep learning method, greatly advancing traffic prediction abilities. The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives. Specifically, we first summarize the existing traffic prediction methods, and give a taxonomy. Second, we list the state-of-the-art approaches in different traffic prediction applications. Third, we comprehensively collect and organize widely used public datasets in the existing literature to facilitate other researchers. Furthermore, we give an evaluation and analysis by conducting extensive experiments to compare the performance of different methods on a real-world public dataset. Finally, we discuss open challenges in this field.

arXiv commentto be published in IEEE Transactions on Intelligent Transportation Systems

References in corpus (23)

• Neural Machine Translation by Jointly Learning to Align and Translate
• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
• T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
• Spectral Networks and Locally Connected Networks on Graphs
• Convolutional Sequence to Sequence Learning
• Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
• Deep and Confident Prediction for Time Series at Uber
• How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
• GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
• ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed
• Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
• Dynamical functional prediction and classification, with application to traffic flow prediction
• Learning Geo-Contextual Embeddings for Commuting Flow Prediction
• Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery
• Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network
• Bayesian Particle Tracking of Traffic Flows
• Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
• Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
• Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism
• A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif-based Graph Convolutional Recurrent Neural Network
• Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units

Cited by in corpus (15)

• Graph Neural Network for Traffic Forecasting: A Survey
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
• Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
• Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
• Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities
• Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap
• DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps
• MoGERNN: An Inductive Traffic Predictor for Unobserved Locations
• Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
• Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model
• Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
• Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction
• PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction
• Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges
• Data Matters: The Case of Predicting Mobile Cellular Traffic