Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ. La Rochelle, France.
Further Improvements
If you would like to list your publication related to this topic on this website, please send me your publication in .pdf and I will add the reference.
Fair Use Policy
As this website gives many information that come from my research, please cite my following paper:
B. Remmouche, D. Boukraa, A. Zakharova, T. Bouwmans, M. Taffar, "Long-Term Spatio-Temporal Graph Attention Network for Traffic Forecasting", Expert Systems with Applications, 2025.
My recent publications are available on Academia, ResearchGate, Researchr, ORCID and Publication List.
Objective
The aim of this web site is to provide resources such as references (599 papers), datasets (3 datasets), codes (42 codes) and links to demonstration websites ( websites) for the research on traffic forecasting (also known as traffic flow estimation and traffic flow prediction) by grouping all related researches and particularly recent advances in this field. For this, it is organized in the following sections:
A) Challenges (20 papers)
Theoretical Challenges (), Countries Challenges (14 papers), Applications Challenges (6 papers)
B) Learning Models (516 papers)
B.1) Statistical Models (10 papers)
Parametrics Methods (6 papers), No-Parametrics Methods (4 papers)
B.2) Machine Learning Models (144 papers)
Neural Networks (24 papers): Multi-Layer Perceptron (10 papers), Rectified Linear Unit (1 paper), Recurrent Neural Networks (RNN) (3 papers), Multi-Scale Spatio-Temporal Neural Network (1 paper) ,Quantum Neural Networks (1 paper), Gated Recurrent Unit (GRU) (4 papers), Kolmogorov-Arnold Networks (1 paper), Attention Neural Networks (2 papers)
Support Vector Machines (5 papers)
Deep Neural Networks Models (100 papers) : Auto-Encoders (3 papers), Convolutional Neural Networks (CNNs) (12 papers), Long Short-Term Memory (LSTM) (17 papers), Transformers (60 papers), WaveNet (3 papers), Mixture of Experts Networks (5 papers), Physic-Informed Neural Networks (2 papers)
Large Language Models (14 papers)
B.3) Graph Learning Models (355 papers)
Graph Convolutional Networks (179 papers)
Graph Neural Networks (161 papers)
Graph Transformers Networks (12 papers)
Graph Large Language Models (3 papers)
B.4) Hypergraph Learning Models (10 papers)
C) Hybrid Models (10 papers)
D) Available Datasets (4)
Conventional Datasets (1), Large-Scale Datasets (3)
E) Available Implementations (42 implementations)
F) Real-Time Implementations (19 papers)
Federated Mechanisms (16 papers)
Distributed Mechanisms (3 papers)
G) Websites
H) Surveys (20 papers)
I) Studies (11 papers)