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

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