Traffic Forecasting

Traffic forecasting aims to predict future traffic conditions using historical data and potentially external factors, enabling efficient resource allocation and improved transportation management. Current research heavily utilizes deep learning, focusing on graph neural networks (GNNs) and transformers, often combined to leverage both spatial and temporal dependencies within road networks, with a growing emphasis on handling data heterogeneity, out-of-distribution scenarios, and limited sensor coverage. These advancements hold significant potential for improving urban planning, optimizing traffic flow, and enhancing the efficiency of intelligent transportation systems.

Papers

September 8, 2022

August 11, 2022

Uncertainty Quantification for Traffic Forecasting: A Unified Approach
Weizhu Qian, Dalin Zhang, Yan Zhao, Kai Zheng, James J. Q. Yu
Uncertainty Quantification Unified Framework Traffic Forecasting Traffic Data Spatiotemporal Correlation Deep Uncertainty

August 5, 2022

Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting
Juyong Jiang, Binqing Wu, Ling Chen, Kai Zhang, Sunghun Kim
Native Robustness Adversarial Learning Recent Trend Traffic Forecasting Trend Analysis Traffic Forecasting Datasets

July 9, 2022

Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting
Aosong Feng, Leandros Tassiulas
Graph Transformer Traffic Forecasting Temporal Transformer Temporal Correlation Spatial Temporal Graph Adaptive Graph Traffic Flow Forecasting

June 19, 2022

Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting
Meng-Ju Tsai, Zhiyong Cui, Hao Yang, Cole Kopca, Sophie Tien, Yinhai Wang
Traffic Prediction Traffic Forecasting Traffic Flow Forecasting Future Traffic Processing Framework Network Wide Traffic Long Term Traffic Forecasting

June 18, 2022

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, Christian S. Jensen
Traffic Forecasting Temporal Graph Neural Network Temporal Correlation Traffic Network Spatial Temporal Graph Spatiotemporal Correlation

June 11, 2022

RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting
Shreshth Tuli, Matthew R. Wilkinson, Chris Kettell
Traffic Forecasting Traffic Datasets Spatio Temporal Forecasting Incident Detection

June 7, 2022

Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting
Chen Weikang, Li Yawen, Xue Zhe, Li Ang, Wu Guobin
Graph Attention Network Graph Convolution Temporal Graph Traffic Forecasting Attention Network

May 29, 2022

Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction
Ruixing Zhang, Liangzhe Han, Boyi Liu, Jiayuan Zeng, Leilei Sun
Traffic Forecasting Dynamic Graph Learning Dynamic Graph Representation Learning Hierarchical Memory Origin Destination Spatial Propagation Network Deep Spatiotemporal

May 18, 2022

Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting
Aoyu Liu, Yaying Zhang
Dynamic Graph Temporal Graph Traffic Forecasting Traffic Datasets Accurate Traffic Forecasting

May 3, 2022

Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting
Wei Zhao, Shiqi Zhang, Bing Zhou, Bei Wang
Temporal Dependency Traffic Forecasting

April 4, 2022

Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting
Tanwi Mallick, Prasanna Balaprakash, Jane Macfarlane
Uncertainty Quantification Traffic Forecasting Convolutional Recurrent Short Term Traffic Spatiotemporal Graph Neural Network Diffusion Convolutional Recurrent Neural Network

February 25, 2022

Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting
Jiabin Tang, Tang Qian, Shijing Liu, Shengdong Du, Jie Hu, Tianrui Li
Graph Neural Network Traffic Forecasting Temporal Graph Learning Latent Graph Accurate Traffic Forecasting

February 18, 2022

PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting
Yuyol Shin, Yoonjin Yoon
Traffic Prediction Traffic Forecasting Traffic Flow Forecasting Temporal Traffic Progressive Graph

February 11, 2022

A Graph-based U-Net Model for Predicting Traffic in unseen Cities
Luca Hermes, Barbara Hammer, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling
Traffic Forecasting Traffic Data Traffic Efficiency Spatial Generalization Accurate Traffic Prediction Road Topology Graph U Net

February 8, 2022

Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities
Yihong Tang, Ao Qu, Andy H. F. Chow, William H. K. Lam, S. C. Wong, Wei Ma
Urban Environment Task Transferability Traffic Forecasting Internet Service Domain Accurate Traffic Prediction Accurate Traffic Forecasting Short Term Traffic Network Wide Traffic Transferable Time Series

January 17, 2022

SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers
Alabi Bojesomo, Hasan Al Marzouqi, Panos Liatsis
Vision Transformer Swin Transformer Traffic Prediction Traffic Forecasting Spatiotemporal Forecasting Shifted Window Transformer

January 11, 2022

A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles
Eric L. Manibardo, Ibai Laña, Esther Villar, Javier Del Ser
Traffic Forecasting Traffic Pattern Graph Based Method Traffic Estimation Sensorless Estimation

December 30, 2021

A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting
Guanyao Li, Shuhan Zhong, S. -H. Gary Chan, Ruiyuan Li, Chih-Chieh Hung, Wen-Chih Peng
Temporal Dependency Traffic Forecasting Temporal Transformer

Traffic Forecasting

Papers

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model

Uncertainty Quantification for Traffic Forecasting: A Unified Approach

Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting

Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting

Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting

Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting

Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction

Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting

Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting

Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting

Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting

PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting

A Graph-based U-Net Model for Predicting Traffic in unseen Cities

Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities

SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers

A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles

A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting