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Table 1 List of methodology, ML and DL used by each reviewed literature

From: Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

No Authors Model name Machine learning and deep learning
A B C D E F G H I J K L M N O P Q R S T U V W
1 Tu et al. (2021) SG-CNN X                       
2 Zhang et al. (2021) Multi-city Traffic Flow Forecasting Network (MTN) X X                      
3 Hou et al. (2021) SAE-RBF Framework        X X                
4 Sun et al. (2021) Congestion Patern Prediction          X               
5 Xia et al. (2021) WND-LSTM   X                      
6 Romo et al. (2020a) Traffic Speed Prediction Framework X X   X                    
7 Abdelwahab et al. (2020b) Traffic Congestion Classification based on compact image representation X                       
8 Abdellah and Koucheryavy (2020) IoT traffic prediction   X                      
9 Wang et al. (2020) Multitask Deep Learning Model (MTGCN)       X                  
10 Lin, Wang, et al. (2020) Traffic flow prediction model (LSTM_SPLSTM)   X                      
11 Qu et al. (2020) Local and Global Spatial Temporal Network (LGSTN)   X     X                  
12 Wang et al. (2020) Improved traffic state identification               X X X        
13 Shin et al. (2020) LSTM-based traffic flow prediction   X                      
14 Ranjan et al. (2020) Hybrid neural network for the purpose of spatial and temporal information extraction X X                X       
15 Liu et al. (2020) Deep Learning Network   X     X                  
16 Elleuch et al. (2020) Intelligent Traffic Congestion Prediction System using Floating Car Data (FCD) X     X      X X             
17 Sun et al. (2020) Selected Stacked Gated Recurrent Units model (SSGRU)                    X     
18 Zafar and Haq (2020) Traffic congestion prediction case study    X X          X X       X    
19 Jingjuan Wang and Chen (2020) Varying spatiotemporal graph-based convolution model (VSTGC)       X                  
20 Essien (2020) Deep learning urban traffic prediction model combined with tweet information   X      X                 
21 Ren and Xie (2019) Transfer Knowledge Graph Neural Network (TKGNN) X                       
22 Chou et al. (2019) Deep Ensemble Stacked Long Short-Term Memory (DE-LSTM)   X                      
23 Yi and Bui (2019) Vehicle Detection System (VDS)   X          X             
24 Xu et al. (2019) End-to-end neural network named C-LSTM X X                      
25 Jingyuan Wang et al. (2019) Deep urban traffic flow prediction (DST) based on spatial temporal features X X                      
26 Yang et al. (2019) CNN-based multi-feature predictive model (MF-CNN) X                       
27 Chen et al. (2019) Multiple residual recurrent graph neural networks (Mres-RGNN) X      X      X         X     
28 Bartlett et al. (2019) ML method comparative study      X         X      X      
29 Xu et al. (2018a) Treating network status as a video for prediction of congestion level             X X           
30 Shirazi and Morris (2018) Feature collection system X          X              
31 Tampubolon and Hsiung (2018) Supervised Deep Learning Based Traffic Flow Prediction (SDLTFP)           X              
32 Jin et al. (2018) Spatio Temporal Recurrent Convolutional Network (STRCN) X X                      
33 Duan et al. (2018) Deep hybrid neural network X X                      
34 Chen et al. (2018) Fuzzy Deep convolutional Network (FDCN) X                     X   
35 Kong et al. (2018) Intelligent Traffic Recommendation System   X                      
36 Tian et al. (2018) Traffic flow forecasting   X                      
37 Khan et al. (2017) Framework that integrates CV with AI               X         X  
38 Lawe and Wang (2016) Deep-learning neural network for traffic flow optimization                        X
39 Wang et al. (2016) Traffic Condition Estimation Integrated with GPS and tweet          X               
Total 15 18 1 2 2 5 2 1 2 3 3 1 3 3 1 2 1 1 2 1 1 1 1
  1. (A) Convolutional Neural Network [CNN]; (B) Long-Short Term Memory [LSTM]; (C) Random Forest [RF]; (D) XGBoost; (E) Artificial Neural Network [ANN]; (F) Graph Convolution Network [GCN]; (G) Sparse Autoencoder [SAE]; (H) Radial Basis Function [RBF]; (I) Hidden Markov Model; (J) Deep Neural Network [DNN]; (K) Recurrent Neural Network [RNN]; (L) C4.5; (M) K-Nearest Neighbour [KNN]; (N) Support Vector Machine [SVM]; (O) Fuzzy C-Means; (P) Random Subspace; (Q) Transpose CNN; (R) Support Vector Regression [SVR]; (S) Gated Recurrent Unit [GRU]; (T) Gradient Boost; (U) Fuzzy Network; (V) Case-Based Reasoning [CBR]; (W) Multi-Task Learning