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2004.02391
Cited By

Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep
Learning Approach Considering Dynamic Non-Local Spatial Correlation and
Non-Stationary Temporal Dependency

Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep Learning Approach Considering Dynamic Non-Local Spatial Correlation and Non-Stationary Temporal Dependency

Transportation Research Part C: Emerging Technologies (Transp. Res. Part C), 2020

6 April 2020

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Papers citing "Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep Learning Approach Considering Dynamic Non-Local Spatial Correlation and Non-Stationary Temporal Dependency"

4 / 4 papers shown

Traffic Prediction using Artificial Intelligence: Review of Recent
Advances and Emerging Opportunities

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging OpportunitiesTransportation Research Part C: Emerging Technologies (TRC), 2022

Xiaoliang (George) Zhao

345

190

0

31 May 2023

Towards better traffic volume estimation: Jointly addressing the
underdetermination and nonequilibrium problems with correlation-adaptive GNNs

Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNsTransportation Research Part C: Emerging Technologies (TRC), 2023

Tong Nie

360

22

0

10 Mar 2023

Graph Neural Network for Traffic Forecasting: A Survey

Graph Neural Network for Traffic Forecasting: A SurveyExpert systems with applications (ESWA), 2021

GNN AI4TS AI4CE

874

1,189

0

27 Jan 2021

Deep Learning for Road Traffic Forecasting: Does it Make a Difference?

Deep Learning for Road Traffic Forecasting: Does it Make a Difference?

Eric L. Manibardo

275

90

0

02 Dec 2020

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