Spatiotemporal Traffic

Spatiotemporal traffic analysis focuses on predicting and understanding traffic patterns across space and time, aiming to improve transportation efficiency and safety. Current research heavily utilizes deep learning architectures, such as graph convolutional networks and transformers, often incorporating techniques like tensor decomposition and dynamic regression to model complex spatial and temporal dependencies within traffic data. These advancements are improving the accuracy of traffic forecasts and enabling better understanding of traffic dynamics, with implications for urban planning, intelligent transportation systems, and resource allocation. Furthermore, research is actively addressing challenges like data sparsity, adversarial attacks, and uncertainty quantification to enhance the robustness and reliability of predictive models.

Papers

November 14, 2024

Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting
Weilin Ruan, Wenzhuo Wang, Siru Zhong, Wei Chen, Li Liu, Yuxuan Liang
Spatio Temporal Time Matter Temporal Dependency State of the Art Forecasting Temporal Graph Neural Network Traffic Flow Understanding Cross Spatiotemporal Traffic

November 6, 2024

Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
Yang He, Chengchuan An, Yuheng Jia, Jiachao Liu, Zhenbo Lu, Jingxin Xia
High Efficiency Vehicle Trajectory Data Spatiotemporal Traffic Oblique Unmanned Aerial Vehicle Image Traffic Forecasting Datasets

August 29, 2024

Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecasting
Osama Ahmad, Zubair Khalid
Deep Convolutional Neural Network Graph Convolutional Network Traffic Prediction Traffic Data Variational Graph Variational Mode Decomposition Spatiotemporal Traffic

July 13, 2024

ST-RetNet: A Long-term Spatial-Temporal Traffic Flow Prediction Method
Baichao Long, Wang Zhu, Jianli Xiao
Long Term Traffic Flow Forecasting Temporal Traffic Spatiotemporal Traffic Retentive Network

May 9, 2024

FusionTransNet for Smart Urban Mobility: Spatiotemporal Traffic Forecasting Through Multimodal Network Integration
Binwu Wang, Yan Leng, Guang Wang, Yang Wang
Hybrid Fusion Temporal Interaction Spatiotemporal Traffic Urban Transportation

August 20, 2023

Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis
Sumin Han, Youngjun Park, Minji Lee, Jisun An, Dongman Lee
Graph Convolution Traffic Prediction Traffic Pattern Spatiotemporal Traffic

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

June 25, 2023

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

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 12, 2022

Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
Qin Li, Xuan Yang, Yong Wang, Yuankai Wu, Deqiang He
Tensor Decomposition Traffic Flow Prediction Adjacency Matrix Temporal Traffic Graph Fusion Spatiotemporal Traffic

November 23, 2022

RegTraffic: A Regression Based Traffic Simulator for Spatiotemporal Traffic Modeling, Simulation and Visualization
Sifatul Mostafi, Taghreed Alghamdi, Khalid Elgazzar
Simulation Study Interactive Visualization Traffic Simulation Spatiotemporal Traffic

October 21, 2022

Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach
Tong Nie, Guoyang Qin, Yunpeng Wang, Jian Sun
Constructive Approach Many Sparse Traffic Datasets Low Rank Tensor Vehicle Speed Spatiotemporal Traffic Speed Deviation Pattern

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

May 19, 2022

Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns
Tong Nie, Guoyang Qin, Jian Sun
Constructive Approach Complex Pattern Missing Value Tensor Based Spatiotemporal Traffic Tensor Rank Low Rank Tensor Completion Schatten $P$ Norm Traffic Data Imputation

March 20, 2022

Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting
Xinyu Chen, Chengyuan Zhang, Xi-Le Zhao, Nicolas Saunier, Lijun Sun
Matrix Factorization Multivariate Time Series Forecasting Non Stationarity Spatiotemporal Traffic

Spatiotemporal Traffic

Papers

Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting

Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data

Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecasting

ST-RetNet: A Long-term Spatial-Temporal Traffic Flow Prediction Method

FusionTransNet for Smart Urban Mobility: Spatiotemporal Traffic Forecasting Through Multimodal Network Integration

Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis

Uncertainty Quantification for Image-based Traffic Prediction across Cities

Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training

Enhancing Deep Traffic Forecasting Models with Dynamic Regression

Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition

RegTraffic: A Regression Based Traffic Simulator for Spatiotemporal Traffic Modeling, Simulation and Visualization

Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach

Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models

Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns

Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting