Traffic Speed

Accurate traffic speed prediction is crucial for optimizing transportation systems and improving urban mobility. Current research focuses on improving prediction accuracy and interpretability by incorporating diverse data sources (e.g., weather, incidents) and leveraging advanced deep learning architectures such as Graph Neural Networks (GNNs), Transformers, and Recurrent Neural Networks (RNNs), often combined with techniques like Mixture of Experts and knowledge graphs to handle complex spatiotemporal dependencies. These advancements aim to enhance the reliability of traffic forecasts, leading to better traffic management, reduced congestion, and improved efficiency in transportation networks.

Papers

September 5, 2024

Interpretable mixture of experts for time series prediction under recurrent and non-recurrent conditions
Zemian Ke, Haocheng Duan, Sean Qian
Expert Knowledge Mixture Model Time Series Prediction Expert Model Recurrent Event Traffic Speed Speed Map

July 25, 2024

Context-aware knowledge graph framework for traffic speed forecasting using graph neural network
Yatao Zhang, Yi Wang, Song Gao, Martin Raubal
Neural Network Knowledge Graph Graph Drawing Traffic Forecasting Traffic Speed

July 16, 2024

Accounting for Work Zone Disruptions in Traffic Flow Forecasting
Yuanjie Lu, Amarda Shehu, David Lattanzi
Graph Convolutional Network Traffic Flow Traffic Flow Forecasting Traffic Speed Work Zone

June 24, 2024

Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
Yicheng Zhou, Pengfei Wang, Hao Dong, Denghui Zhang, Dingqi Yang, Yanjie Fu, Pengyang Wang
Graph Neural Network Topological Metric Traffic Speed

June 17, 2024

Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations
Qinhua Jiang, Brian Yueshuai He, Changju Lee, Jiaqi Ma
High Efficiency Traffic Forecasting Traffic Network Hurricane Evacuation Traffic Speed Accurate Traffic Prediction Traffic Volume Prediction

May 1, 2024

Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
Rushan Wang, Yanan Xin, Yatao Zhang, Fernando Perez-Cruz, Martin Raubal
Counterfactual Explanation Traffic Prediction Traffic Forecasting Traffic Speed

April 22, 2024

ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction
Yi Rong, Yingchi Mao, Yinqiu Liu, Ling Chen, Xiaoming He, Dusit Niyato
Traffic Speed Interpretable Causal Speed Map Temporal Spatial Causal

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

April 21, 2023

An Incomplete Tensor Tucker decomposition based Traffic Speed Prediction Method
Jiajia Mi
Intelligent Transportation System Temporal Traffic Tucker Decomposition Traffic Speed

December 25, 2022

Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
Dongkun Wang, Wei Fan, Pengyang Wang, Pengfei Wang, Dongjie Wang, Denghui Zhang, Yanjie Fu
Spatial Transformer Traffic Speed Implicit Correlation

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 28, 2022

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Hongjun Wang, Jiyuan Chen, Tong Pan, Zipei Fan, Boyuan Zhang, Renhe Jiang, Lingyu Zhang, Yi Xie, Zhongyi Wang, Xuan Song
Deep Learning Structured Dropout Graph Model Spatio tempOral Spatial Temporal Graph Traffic Speed

November 6, 2022

Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach
Bin Lei, Shaoyi Huang, Caiwen Ding, Monika Filipovska
Graph Attention Network Temporal Dependency Traffic Prediction Traffic Network Traffic Speed Traffic Volume Prediction

October 1, 2022

STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting
Ruikang Luo, Yaofeng Song, Liping Huang, Yicheng Zhang, Rong Su
Spatio Temporal Long Term Prediction Traffic Network Spatio tempOral Traffic Speed

September 27, 2022

EgoSpeed-Net: Forecasting Speed-Control in Driver Behavior from Egocentric Video Data
Yichen Ding, Ziming Zhang, Yanhua Li, Xun Zhou
Driver Behavior Traffic Speed Ego Velocity

September 5, 2022

HAGCN : Network Decentralization Attention Based Heterogeneity-Aware Spatiotemporal Graph Convolution Network for Traffic Signal Forecasting
JunKyu Jang, Sung-Hyuk Park
Heterogeneous Graph Attention Network Graph Convolution Network Traffic Speed Spatiotemporal Network

July 22, 2022

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction
Pengyu Fu, Liang Chu, Zhuoran Hou, Jincheng Hu, Yanjun Huang, Yuanjian Zhang
Intersection Scenario Spatial Information Traffic Speed

December 4, 2021

Understanding Dynamic Spatio-Temporal Contexts in Long Short-Term Memory for Road Traffic Speed Prediction
Won Kyung Lee, Deuk Sin Kwon, So Young Sohn
Long Short Term Memory Traffic Flow Prediction Spatio Temporal Context Prediction Method Traffic Speed

November 3, 2021

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies
Shatrughan Modi, Jhilik Bhattacharya, Prasenjit Basak
Latent Space Constructive Approach Temporal Dependency Traffic Prediction Traffic Flow Prediction Traffic Speed Accurate Traffic Prediction Accurate Traffic Forecasting

Traffic Speed

Papers

Interpretable mixture of experts for time series prediction under recurrent and non-recurrent conditions

Context-aware knowledge graph framework for traffic speed forecasting using graph neural network

Accounting for Work Zone Disruptions in Traffic Flow Forecasting

Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction

Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations

Counterfactual Explanations for Deep Learning-Based Traffic Forecasting

ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction

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

An Incomplete Tensor Tucker decomposition based Traffic Speed Prediction Method

Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations

Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach

STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting

EgoSpeed-Net: Forecasting Speed-Control in Driver Behavior from Egocentric Video Data

HAGCN : Network Decentralization Attention Based Heterogeneity-Aware Spatiotemporal Graph Convolution Network for Traffic Signal Forecasting

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

Understanding Dynamic Spatio-Temporal Contexts in Long Short-Term Memory for Road Traffic Speed Prediction

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies