Journal of Big Data

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
No	Authors	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

(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

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