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

May 9, 2023

Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)
Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz, Flora D. Salim
Traffic Forecasting Spatial Encoder Built Road Deep Time Series Forecasting

May 1, 2023

Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction
Weiheng Zhong, Hadi Meidani, Jane Macfarlane
Neural ODE Graph Attention Traffic Forecasting Traffic Datasets Traffic Pattern Attention Graph

March 20, 2023

Graph Neural Rough Differential Equations for Traffic Forecasting
Jeongwhan Choi, Noseong Park
Spatio Temporal Traffic Forecasting Spatio Temporal Graph Neural Rough Differential Equation

March 19, 2023

AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting
Juan S. Angarita-Zapata, Antonio D. Masegosa, Isaac Triguero
Machine Learning Diverse Ensemble Traffic Prediction Traffic Forecasting Machine Learning Pipeline End to End AutoML Auto Sklearn

March 16, 2023

Adaptive Modeling of Uncertainties for Traffic Forecasting
Ying Wu, Yongchao Ye, Adnan Zeb, James J. Q. Yu, Zheng Wang
Traffic Prediction Traffic Forecasting High Uncertainty Value Prediction Uncertainty Adaptive Model

February 25, 2023

Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting
Haiyang Liu, Chunjiang Zhu, Detian Zhang, Qing Li
Spatio Temporal Temporal Dependency Temporal Attention Traffic Forecasting

February 20, 2023

Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting
Arian Prabowo, Wei Shao, Hao Xue, Piotr Koniusz, Flora D. Salim
Graph Transformer Graph Attention Network Traffic Forecasting Dynamic Model High Quality Sensor Similar Pair Temporal Traffic

February 15, 2023

Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction
Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Petar Popovski, Shlomo Shamai
Conformal Prediction Low Latency Traffic Forecasting Dynamic Scheduling

January 30, 2023

Do We Really Need Graph Neural Networks for Traffic Forecasting?
Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, Roger Zimmermann
Graph Neural Network Traffic Forecasting Spatio Temporal Graph Spatio Temporal Learning Spatial Correlation Spatial Learning

January 17, 2023

Enhancing Deep Traffic Forecasting Models with Dynamic Regression
Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun
Traffic Prediction Traffic Forecasting Spatiotemporal Traffic

December 13, 2022

Graph Convolutional Networks for Traffic Forecasting with Missing Values
Jingwei Zuo, Karine Zeitouni, Yehia Taher, Sandra Garcia-Rodriguez
Graph Neural Network Traffic Forecasting Traffic Data Missing Value Graph Convolution Network

December 12, 2022

GT-CausIn: a novel causal-based insight for traffic prediction
Ting Gao, Rodrigo Kappes Marques, Lei Yu
Long Form Novel Traffic Prediction Traffic Forecasting Temporal Network Traffic Datasets Spatio Temporal Forecasting Causal Insight

December 10, 2022

Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting
Seongjin Choi, Nicolas Saunier, Vincent Zhihao Zheng, Martin Trepanier, Lijun Sun
Potential Scalability Traffic Forecasting Mixture Model Covariance Conditioning Spatiotemporal Forecasting Traffic Speed Long Term Traffic Forecasting

November 27, 2022

Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura
Traffic Forecasting Traffic Datasets Temporal Graph Learning Graph Meta Learning Meta Weight Graph Neural Network

October 31, 2022

Large scale traffic forecasting with gradient boosting, Traffic4cast 2022 challenge
Martin Lumiste, Andrei Ilie
Challenge Task Traffic Prediction Traffic Forecasting Gradient Boosting Traffic4cast Competition Accurate Traffic Prediction Accurate Traffic Forecasting

October 30, 2022

Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting
Leyan Deng, Chenwang Wu, Defu Lian, Min Zhou
Graph Attention Network Traffic Forecasting Traffic Pattern Traffic4cast Competition Intrinsic Feature Transposed Convolution

October 28, 2022

Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability
Ibai Laña, Ignacio, Olabarrieta, Javier Del Ser
Uncertainty Estimation Barzilai Borwein Technique Traffic Prediction Traffic Forecasting Traffic Data High Confidence Uncertainty Estimation Method Actionable Part Experimental Comparison

October 6, 2022

Spatial-Temporal Graph Convolutional Gated Recurrent Network for Traffic Forecasting
Le Zhao, Mingcai Chen, Yuntao Du, Haiyang Yang, Chongjun Wang
Temporal Dependency Traffic Forecasting Spatial Temporal Graph Spatial Dependence

October 5, 2022

Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
Fan Liu, Hao Liu, Wenzhao Jiang
Traffic Forecasting Adversarial Scenario Spatiotemporal Traffic Adversarial Traffic

September 12, 2022

Residual Correction in Real-Time Traffic Forecasting
Daejin Kim, Youngin Cho, Dongmin Kim, Cheonbok Park, Jaegul Choo
Convolutional Neural Network Traffic Forecasting Temporal Convolutional Network Residual Stream