Introduction
RQ1 | In studies on traffic flow prediction that implemented machine learning algorithms and techniques, what are the gaps found in each study which can be further improved in terms of methods, challenges, evaluation approach and results? |
RQ2 | What are the main ML techniques and parameters used in the highlighted research, particularly for traffic flow prediction? |
RQ3 | How is the effectiveness of the proposed methodology evaluated and how did the proposed solution influence traffic management in urban cities? |
Search methodology
Literature inclusion and exclusion criteria
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Articles are indexed and published as journal articles and proceedings. Unpublished literatures are excluded.
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Articles are focused on ‘traffic flow prediction using machine learning’ within the domain and context of transportation and computer science. Articles on traffic flow prediction unrelated to transportation and computer science are excluded.
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Articles are easily accessible by the researcher, either through public/open access or institution access. Access that requires payment is excluded.
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Articles must contain basic sections of literature, namely, the abstract, introduction, methodology, results, discussion and conclusion. Articles that lack the required sections are excluded.
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Articles must be written in English. Articles written in languages other than English are excluded.
Study selection
Gaps, techniques, evaluation and opportunities
No | Authors | Model name | Machine learning and deep learning | ||||||||||||||||||||||
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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 |
Gaps, methods, challenges and evaluation methodology for traffic flow prediction
Gaps in SLR for Traffic flow prediction using machine learning
Techniques in SLR for traffic flow prediction using machine learning
Machine learning approach
Hybrid/ensemble machine learning approach
Machine learning approach with the inclusion of social media
Evaluation from SLR for traffic flow prediction using machine and deep learning
Commonly used ML and DL techniques
Parameters
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 |
Comparison with baseline model
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Historical Average (HA): A time-series model that forecasts demands using values of previous demands at a given location during a similar time interval.
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Autoregressive Integrated Moving Average (ARIMA): A time-series model composed of a combination of autoregressive components and moving average in time-series modelling for traffic flow prediction.
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Support Vector Regression (SVR): A model that is based on linear SVM in performing regression tasks.
Evaluation metric in SLR for traffic flow prediction using machine learning
No | Authors | Evaluation metrics | ||||||||||||||||
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A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | ||
1 | Tu et al. (2021) | X | X | X | ||||||||||||||
2 | Zhang et al. (2021) | X | X | |||||||||||||||
3 | Hou et al. (2021) | X | X | X | X | X | ||||||||||||
4 | Sun et al. (2021) | X | ||||||||||||||||
5 | Xia et al. (2021) | X | X | X | X | |||||||||||||
6 | Romo et al. (2020a) | X | X | X | ||||||||||||||
7 | Abdelwahab et al. (2020b) | X | ||||||||||||||||
8 | Abdellah and Koucheryavy (2020) | X | 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 | X | X | ||||||||||||
13 | Shin et al. (2020) | X | ||||||||||||||||
14 | Ranjan et al. (2020) | X | X | X | ||||||||||||||
15 | Liu et al. (2020) | X | X | X | ||||||||||||||
16 | Elleuch et al. (2020) | X | ||||||||||||||||
17 | Sun et al. (2020) | X | ||||||||||||||||
18 | Zafar and Haq (2020) | X | ||||||||||||||||
19 | Jingjuan Wang and Chen (2020) | X | X | X | ||||||||||||||
20 | Essien (2020) | X | X | X | ||||||||||||||
21 | Ren and Xie (2019) | X | X | X | ||||||||||||||
22 | Chou et al. (2019) | 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 | |||||||||||||||
26 | Yang et al. (2019) | X | X | X | ||||||||||||||
27 | Chen et al. (2019) | X | X | X | ||||||||||||||
28 | Bartlett et al. (2019) | X | ||||||||||||||||
29 | Xu et al. (2018a) | X | ||||||||||||||||
30 | Shirazi and Morris (2018) | X | ||||||||||||||||
31 | Tampubolon and Hsiung (2018) | X | X | X | ||||||||||||||
32 | Jin et al. (2018) | X | ||||||||||||||||
33 | Duan et al. (2018) | X | ||||||||||||||||
34 | Chen et al. (2018) | X | X | X | ||||||||||||||
35 | Kong et al. (2018) | X | X | |||||||||||||||
36 | Tian et al. (2018) | X | X | X | ||||||||||||||
37 | Khan et al. (2017) | X | ||||||||||||||||
38 | Lawe and Wang (2016) | X | ||||||||||||||||
39 | Wang et al. (2016) | X | X | X | ||||||||||||||
Total | 18 | 29 | 16 | 2 | 2 | 3 | 1 | 7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 |