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

November 6, 2023

MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization
Dongcheng Zou, Senzhang Wang, Xuefeng Li, Hao Peng, Yuandong Wang, Chunyang Liu, Kehua Sheng, Bo Zhang
Traffic Prediction Traffic Forecasting Temporal Transformer Traffic Datasets Attention Computation Spatiotemporal Pattern Entropy Optimization

October 31, 2023

Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?
Guopeng Li, Victor L. Knoop, J. W. C., van Lint
Raw Data Traffic Prediction Traffic Forecasting Traffic Data Traffic Datasets Network Wide Traffic Loop Detection

October 26, 2023

Improving Traffic Density Forecasting in Intelligent Transportation Systems Using Gated Graph Neural Networks
Razib Hayat Khan, Jonayet Miah, S M Yasir Arafat, M M Mahbubul Syeed, Duc M Ca
Graph Neural Network Traffic Prediction Traffic Forecasting Intelligent Transportation System Accurate Traffic Prediction

October 24, 2023

Spatial-Temporal Hypergraph Neural Network for Traffic Forecasting
Chengzhi Yao, Zhi Li, Junbo Wang
Temporal Dependency Traffic Forecasting Hypergraph Neural Network Traffic Dynamic

October 18, 2023

Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach
Syed Islam, Monika Filipovska
Attention Based Traffic Forecasting Transportation Network Exogenous Variable Exogenous Information Network Wide Traffic

September 20, 2023

Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
Qian Ma, Zijian Zhang, Xiangyu Zhao, Haoliang Li, Hongwei Zhao, Yiqi Wang, Zitao Liu, Wanyu Wang
Temporal Dependency Traffic Forecasting Spatio Temporal Prediction Spatial Pattern Sensing Model

September 16, 2023

Test-Time Compensated Representation Learning for Extreme Traffic Forecasting
Zhiwei Zhang, Weizhong Zhang, Yaowei Huang, Kani Chen
Time Series Traffic Prediction Traffic Forecasting Test Time Training Traffic Video Spatiotemporal Correlation

August 31, 2023

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
Weijia Zhang, Le Zhang, Jindong Han, Hao Liu, Yanjie Fu, Jingbo Zhou, Yu Mei, Hui Xiong
Traffic Forecasting Accurate Traffic Forecasting Traffic State Sequence

August 29, 2023

A Comparative Study of Loss Functions: Traffic Predictions in Regular and Congestion Scenarios
Yangxinyu Xie, Tanwi Mallick
Loss Function Comparative Study Traffic Prediction Traffic Forecasting Traffic Datasets Traffic Speed Traffic Congestion Congestion Prediction

August 28, 2023

Meta Attentive Graph Convolutional Recurrent Network for Traffic Forecasting
Adnan Zeb, Yongchao Ye, Shiyao Zhang, James J. Q. Yu
Traffic Prediction Traffic Forecasting Pattern Exploiting Training

August 21, 2023

STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
Hangchen Liu, Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, Xuan Song
Spatio Temporal Traffic Forecasting Traffic Flow Forecasting Temporal Traffic Vanilla Transformer Traffic Forecasting Datasets

August 17, 2023

Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank
Zhanyu Liu, Guanjie Zheng, Yanwei Yu
Traffic Forecasting Traffic Data Traffic Datasets Cross City Cross City Few Shot

August 14, 2023

ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting
Zepu Wang, Yuqi Nie, Peng Sun, Nam H. Nguyen, John Mulvey, H. Vincent Poor
Spatio Temporal Traffic Forecasting Temporal Graph Neural Network Multi Layer Perceptron Accurate Traffic Forecasting

August 11, 2023

Uncertainty Quantification for Image-based Traffic Prediction across Cities
Alexander Timans, Nina Wiedemann, Nishant Kumar, Ye Hong, Martin Raubal
Uncertainty Quantification Urban Environment Traffic Prediction Traffic Forecasting Traffic Datasets Traffic Pattern Spatiotemporal Traffic

July 27, 2023

HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
Zezhi Shao, Fei Wang, Zhao Zhang, Yuchen Fang, Guangyin Jin, Yongjun Xu
Traffic Forecasting Hierarchical Transformer Short Term Traffic Long Term Traffic Forecasting

July 8, 2023

Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction
Bo Wang, A. K. Qin, Sajjad Shafiei, Hussein Dia, Adriana-Simona Mihaita, Hanna Grzybowska
Neural Network Training Data Physic Informed Neural Network Traffic Forecasting Multi Task Optimization PINN Training

July 3, 2023

ESGCN: Edge Squeeze Attention Graph Convolutional Network for Traffic Flow Forecasting
Sangrok Lee, Ha Young Kim
Graph Convolutional Network Traffic Forecasting Spatiotemporal Representation Traffic Flow Forecasting

July 2, 2023

DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting
Binqing Wu, Ling Chen
Temporal Dependency Traffic Forecasting

June 25, 2023

Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training
Fan Liu, Weijia Zhang, Hao Liu
Adversarial Attack Traffic Forecasting Spatiotemporal Traffic

June 15, 2023

Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting
Yirong Chen, Ziyue Li, Wanli Ouyang, Michael Lepech
Traffic Forecasting Spatiotemporal Modeling Accurate Traffic Forecasting

Traffic Forecasting

Papers

MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization

Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?

Improving Traffic Density Forecasting in Intelligent Transportation Systems Using Gated Graph Neural Networks

Spatial-Temporal Hypergraph Neural Network for Traffic Forecasting

Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach

Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting

Test-Time Compensated Representation Learning for Extreme Traffic Forecasting

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network

A Comparative Study of Loss Functions: Traffic Predictions in Regular and Congestion Scenarios

Meta Attentive Graph Convolutional Recurrent Network for Traffic Forecasting

STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank

ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting

Uncertainty Quantification for Image-based Traffic Prediction across Cities

HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting

Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction

ESGCN: Edge Squeeze Attention Graph Convolutional Network for Traffic Flow Forecasting

DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting

Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training

Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting