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2012.02260
Cited By

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

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

2 December 2020

Eric L. Manibardo

ArXiv (abs)PDF HTML

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

17 / 17 papers shown

Benchmarking M-LTSF: Frequency and Noise-Based Evaluation of Multivariate Long Time Series Forecasting Models

Benchmarking M-LTSF: Frequency and Noise-Based Evaluation of Multivariate Long Time Series Forecasting Models

Melanie Schaller

193

2

0

27 Mar 2026

Error Adjustment Based on Spatiotemporal Correlation Fusion for Traffic Forecasting

Error Adjustment Based on Spatiotemporal Correlation Fusion for Traffic ForecastingInformation Fusion (Inf. Fusion), 2025

L. Miranda-Moreno

131

0

0

25 Oct 2025

A comparative study of deep learning and ensemble learning to extend the horizon of traffic forecasting

A comparative study of deep learning and ensemble learning to extend the horizon of traffic forecasting

Saeed Asadi Bagloee

519

2

0

30 Apr 2025

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

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

247

1

0

31 Oct 2023

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

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

283

6

0

02 Jul 2023

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting

LargeST: A Benchmark Dataset for Large-Scale Traffic ForecastingNeural Information Processing Systems (NeurIPS), 2023

Xu Liu

Bryan Hooi

Roger Zimmermann

264

167

0

14 Jun 2023

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

339

190

0

31 May 2023

Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training
(SCPT)

Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)Data mining and knowledge discovery (DMKD), 2023

425

19

0

09 May 2023

Inverting the Fundamental Diagram and Forecasting Boundary Conditions:
How Machine Learning Can Improve Macroscopic Models for Traffic Flow

Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic FlowAdvances in Computational Mathematics (Adv. Comput. Math.), 2023

316

7

0

21 Mar 2023

Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from
Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle
Detectors

Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle DetectorsNeural Information Processing Systems (NeurIPS), 2023

Christian Eichenberger

Rahul Siripurapu

...

Sepp Hochreiter

293

8

0

14 Mar 2023

Traffic Prediction with Transfer Learning: A Mutual Information-based
Approach

Traffic Prediction with Transfer Learning: A Mutual Information-based Approach

Xiaozhuang Song

James Jianqiao Yu

283

26

0

13 Mar 2023

Context-aware multi-head self-attentional neural network model for next
location prediction

Context-aware multi-head self-attentional neural network model for next location predictionTransportation Research Part C: Emerging Technologies (TRC), 2022

Konrad Schindler

315

50

0

04 Dec 2022

Measuring the Confidence of Traffic Forecasting Models: Techniques,
Experimental Comparison and Guidelines towards Their Actionability

Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability

205

1

0

28 Oct 2022

Experimental Standards for Deep Learning in Natural Language Processing
Research

Experimental Standards for Deep Learning in Natural Language Processing ResearchConference on Empirical Methods in Natural Language Processing (EMNLP), 2022

Elisa Bassignana

Max Müller-Eberstein

Daniel Varab

Rob van der Goot

Christian Hardmeier

307

14

0

13 Apr 2022

Gradient boosting machines and careful pre-processing work best: ASHRAE
Great Energy Predictor III lessons learned

Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned

276

7

0

07 Feb 2022

A Graph-based Methodology for the Sensorless Estimation of Road Traffic
Profiles

A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles

Eric L. Manibardo

Esther Villar-Rodriguez

320

4

0

11 Jan 2022

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

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