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Table 2 List of parameters used by each reviewed literature

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

No Authors Parameters
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z AA BB CC DD EE FF GG HH II JJ KK LL MM NN OO PP QQ RR
1 Tu et al. (2021) X X                                           
2 Zhang et al. (2021)    X X                                         
3 Hou et al. (2021)      X                                        
4 Sun et al. (2021) X X X    X X                                      
5 Xia et al. (2021) X X                                           
6 Romo et al. (2020a)   X   X                                         
7 Abdelwahab et al. (2020b)   X   X                          X                
8 Abdellah and Koucheryavy (2020)                                             X
9 Wang et al. (2020)         X X                                    
10 Lin, Wang et al. (2020)     X                                         X
11 Qu et al. (2020)    X X                                         
12 Wang et al. (2020)   X   X       X                                   
13 Shin et al. (2020)   X                                           
14 Ranjan et al. (2020)            X                                  
15 Liu et al. (2020)   X           X X                                
16 Elleuch et al. (2020)   X X            X X                              
17 Sun et al. (2020) X                                            
18 Zafar and Haq (2020)      X X           X X X X                          
19 Jingjuan Wang and Chen (2020)    X   X    X           X   X X                        
20 Essien (2020)   X   X X      X             X                       
21 Ren and Xie (2019)    X                     X X X X                   
22 Chou et al. (2019)    X   X X           X            X                  
23 Yi and Bui (2019) X X X                          X X                
24 Xu et al. (2019)   X    X                                        
25 Jingyuan Wang et al. (2019)    X X      X         X                            
26 Yang et al. (2019)    X                            X X              
27 Chen et al. (2019)   X                               X             
28 Bartlett et al. (2019) X   X                          X                 
29 Xu et al. (2018a)   X X                               X            
30 Shirazi and Morris (2018)                                   X X          
31 Tampubolon and Hsiung (2018) X X                                   X         
32 Jin et al. (2018)    X                                   X X       
33 Duan et al. (2018)                                        X      
34 Chen et al. (2018)    X                             X          X X    
35 Kong et al. (2018)     X                                         
36 Tian et al. (2018)   X X X         X                                 
37 Khan et al. (2017)   X X        X                                   
38 Lawe and Wang (2016) X     X                                      X   
39 Wang et al. (2016)    X                    X                      X  
Total 8 17 17 10 7 3 1 2 2 3 1 2 1 1 1 2 2 2 1 1 1 2 1 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2
  1. (A) Traffic volume; (B) Speed; (C) Time; (D) Average Flow; (E) Weather; (F) Day; (G) Travel Time; (H) Road Area; (I) Trajectory; (J) Density; (K) Traffic Snapshot; (L) License Plate Recognition; (M) Violation Record; (N) Vehicle ID; (O) Position; (P) Holiday; (Q) Location; (R) Date; (S) Special Condition; (T) Lane marking detection; (U) Brake Light Recognition; (V) Tweet Data; (W) Card ID; (X) Station Number; (Y) Line Number; (Z) Expense; (AA) Temperature; (BB) Vehicle Type; (CC) Occupancy Rate; (DD) Road Section; (EE) External Factors; (FF) Sensor Network; (GG) Travel Link; (HH) Blob; (II) Optical Flow; (JJ) Congestion Degree; (KK) Map Size; (LL) User Count; (MM) Spatial Temporal; (NN) Input Flow; (OO) Output Flow; (PP) Incident or Event Nearby; (QQ) GPS Data; (RR) Number of hidden units in LSTM Layer