In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review
Abstract
:1. Introduction
- The unique characteristics of traffic data and common input data representations in traffic prediction are summarized.
- The statement of traffic prediction problems is generalized.
- A comprehensive overview of seven sub-categories of applications of current deep learning models based on the structures of RNNs in traffic prediction is conducted.
- A detailed comparison between RNNs and other state-of-the-art deep learning models is conducted. In addition to the comparison of RNNs with other state-of-the-art models on public datasets, such as the Performance Measurement System (PeMS, http://pems.dot.ca.gov/ (accessed on 2 March 2024)), we design a comparative study focused on short-term passenger flow prediction using a real-world metro smart card dataset. This study allows us to directly compare the predictive performance of RNNs with other models in a practical, real-world context.
- Transformers excel with long sequences and complex patterns, but RNNs can outperform with shorter sequences and smaller datasets. The metro data used in our comparative study favored LSTM, showing that simpler models can sometimes provide more accurate and efficient predictions. Choosing the right model based on the dataset and resources is crucial.
- The future challenges facing RNNs in traffic prediction and how to deal with these challenges are discussed.
2. Development History of Traffic Prediction
2.1. Statistical Methods
2.2. Traditional Machine Learning Methods
2.3. Deep Learning Methods
3. Problem Statement and Input Data Representation Methods
3.1. Traffic Data and Their Unique Characteristics
- Strong periodicity. Traffic data exhibit significant daily and weekly cycles. Typical patterns include morning and evening rush hours during weekdays and different traffic patterns on weekends. Seasonal variations, such as holiday traffic spikes, also exhibit periodicity.
- Spatial dependencies. While many time series involve only time, traffic data are inherently spatiotemporal. Due to the interconnected nature of road networks, the traffic conditions at one location can highly depend on the traffic conditions at nearby or distant locations.
- Non-stationarity. Traffic patterns change over time and are influenced by factors like urban development, changes in traffic regulations, or the introduction of new infrastructure. This non-stationarity means that the statistical properties of the traffic data (such as mean and variance) can vary, making modeling more challenging.
- Volatility. Traffic data can be highly volatile due to unexpected events such as accidents, roadwork, or weather conditions. These events can cause sudden spikes or drops in traffic flow that are not easily predictable with standard models.
- Heteroscedasticity. Traffic volume variability is not constant over time. It can vary significantly across different times of the day or days of the week, particularly increasing during rush hours and decreasing at night.
- Multivariate influences. Traffic conditions are influenced by a wide range of factors beyond just the number of vehicles on the road. Weather conditions, special events, economic conditions, and social media trends can affect traffic flow and congestion levels.
3.1.1. Spatial Dependencies
- Stacked Vector
- Matrix/Grid Representation
- Graph Representation
3.1.2. Temporal Dependencies
3.2. Common Forms of Input Representations
3.2.1. Time Series
Input Representations | Reference | Techniques |
---|---|---|
Time Series | [51] | RNN |
[52] | RNN | |
[53] | LSTM | |
[54] | LSTM | |
Matrix/Grid-based Sequence | [55] | ResNet |
[56] | LSTM + CNN | |
[57] | LSTM + CNN | |
[58] | LSTM + CNN | |
Graph-based Sequence | [59] | RNN + GNN |
[60] | LSTM + GNN | |
[61] | GRU + GCN | |
[62] | GAT + Attention | |
[63] | GCN + Attention |
3.2.2. Matrix/Grid-Based Sequence
3.2.3. Graph-Based Sequence
3.2.4. Sliding Window
3.3. Problem Statement
4. RNN Structures Used for Traffic Prediction
4.1. RNNs
4.2. LSTMs
4.3. GRUs
4.4. Hybrid Models Including RNN Techniques
4.4.1. RNNs + Traditional ML Techniques
Hybrid Models | Reference | Techniques |
---|---|---|
RNNs + Traditional ML Techniques | [69] | LSTM + K-NN |
[70] | GRU + K-NN | |
RNNs + CNNs | [73] | LSTM + CNN |
[74] | LSTM + CNN | |
RNNs + GNNs | [42] | GRU + GCN |
[75] | LSTM + GNN | |
[76] | LSTM + GNN | |
RNNs + Attention | [77] | LSTM + Attention |
[78] | LSTM + Attention | |
[79] | LSTM + Attention | |
[80] | RNN + GNN + Attention | |
[81] | GRU + GCN + Attention |
4.4.2. RNNs + CNNs
4.4.3. RNNs + GNNs
4.4.4. RNNs + Attention
5. Sub-Areas of Traffic Prediction Applications Using RNNs
5.1. Traffic Flow Prediction
5.2. Passenger Flow Prediction
5.3. OD Demand Prediction
5.4. Traffic Speed Prediction
5.5. Travel Time Prediction
5.6. Traffic Accidents and Congestion Prediction
Application | Reference | Techniques | Temporal | Spatial |
---|---|---|---|---|
Traffic Flow Prediction | [29,32,66] | LSTM | √ | × |
[30,65,83] | GRU | √ | × | |
[31,73,74] | LSTM + CNN | √ | √ | |
[36,37,38] | TCN | √ | × | |
[51,53,54,64] | RNN | √ | × | |
[75,76] | LSTM + GNN | √ | √ | |
[77,78,79,82,89] | LSTM + Attention | √ | √ | |
[84] | RNN + GNN | √ | √ | |
[85] | RNN + Attention | √ | √ | |
[90] | GRU + CNN + Attention | √ | √ | |
Passenger Flow Prediction | [91,92,93,94] | LSTM | √ | × |
[95,96] | LSTM + CNN | √ | √ | |
[97] | RNN + GCN | √ | √ | |
[98] | LSTM + GCN + Attention | √ | √ | |
OD Demand Prediction | [56,57,58] | LSTM+CNN | √ | √ |
[99,100] | LSTM | √ | × | |
[101] | RNN, LSTM, GRU | √ | × | |
[102] | GCN | × | √ | |
[103] | GAN | √ | √ | |
[104] | LSTM + Attention + Conv | √ | √ | |
Traffic Speed Prediction | [28] | LSTM | √ | × |
[33,34] | CNN | √ | × | |
[52] | RNN | √ | × | |
[59] | RNN + GNN | √ | √ | |
[60] | LSTM + GNN | √ | √ | |
[81] | GRU + GCN + Attention | √ | √ | |
[105,106] | RNN, LSTM | √ | × | |
[107] | RNN + CNN | √ | √ | |
[108] | GRU + CNN | √ | √ | |
[109] | LSTM + CNN | √ | √ | |
[110] | LSTM + CNN + Attention | √ | √ | |
[111] | RNN + GNN + Attention | √ | √ | |
Travel Time Prediction | [112,113] | RNN, LSTM | √ | × |
[114,119] | LSTM + Attention | √ | √ | |
[115] | LSTM + Conv | √ | √ | |
[117] | LSTM + GCN | √ | √ | |
[118] | LSTM + CNN | √ | √ | |
Traffic Accident Prediction | [121,122] | RNN | √ | × |
[123] | RNN, LSTM, GRU | √ | × | |
[124] | LSTM + Attention | √ | × | |
[125] | TCN + GCN | √ | √ | |
Traffic Congestion Prediction | [127] | LSTM | √ | × |
[128] | LSTM + CNN | √ | √ | |
[129] | Transformer + GCN | √ | √ | |
Occupancy Prediction | [130,131] | LSTM | √ | × |
[132] | GRU | √ | × | |
[133] | LSTM + MLP | √ | × |
5.7. Occupancy Prediction
6. Discussion
6.1. Discussion: RNNs vs. Transformer Families
- LSTMs are excellent at capturing local temporal patterns and short-term dependencies, which might be prevalent in our metro passenger flow data. Transformers are powerful in capturing long-range dependencies and complex patterns, which might be more useful in datasets with longer historical dependencies or more complex temporal interactions.
- LSTMs tend to perform well with smaller datasets. They can overfit less easily than transformers if the data are limited. Transformers generally require larger datasets to train effectively. They are data hungry and might not perform as well with smaller datasets due to their large number of parameters and high complexity.
- LSTMs can sometimes handle noisy data better due to their gating mechanisms that control the flow of information. Transformers can be sensitive to noise, and their performance might degrade if the data are not clean or highly variable without sufficient training data to generalize well.
- LSTMs have a simpler architecture compared to transformers, which might make them easier to train and tune, especially when computational resources are limited. Transformers, with their self-attention mechanisms and multi-head attention, are more complex and require more computational resources for training. This complexity can be a disadvantage if the computational infrastructure is not robust enough.
- For tasks with clear and strong temporal dependencies, LSTMs can effectively leverage their ability to remember previous states. Transformers can provide better performance for tasks that benefit from capturing broader contextual information and where interactions are not strictly sequential.
6.2. Discussion: RNNs vs. Other Prediction Models
6.3. Challenges and Future Directions
6.3.1. Improve Model Interpretability
6.3.2. Long-Term Dependencies of Traffic Data in Short-Term Traffic Prediction
6.3.3. Lack of a Comprehensive Multi-Scenario Baseline Dataset
6.3.4. Missing Data Problem
6.3.5. Processing Multi-Source Heterogeneous Data
7. Conclusions
- RNNs are extensively utilized in traffic prediction. In processing various types of traffic data, RNNs not only efficiently handle time series data independently but also serve as temporal feature extraction modules when combined with models, such as CNNs and GNNs, for spatiotemporal data processing. Furthermore, RNNs play a significant role across seven sub-areas within traffic prediction.
- RNNs are expected to continue being preferred models for future traffic prediction tasks due to their advantages and will not be replaced by transformers. We conducted a comparative study using real-world metro smart card datasets for short-term passenger flow prediction. This allowed us to directly compare the predictive performance of RNNs (particularly LSTMs) with other models in a real and specific environment. The results showed that, despite the presence of more advanced transformer models, RNNs demonstrated superior performance. This finding underscores the importance of selecting the appropriate model based on the characteristics of different datasets and available resources, as sometimes simpler models can provide more accurate and efficient predictions.
Author Contributions
Funding
Conflicts of Interest
References
- Sun, Y.; Li, X.; Dalal, K.; Xu, J.; Vikram, A.; Zhang, G.; Dubois, Y.; Chen, X.; Wang, X.; Koyejo, S. Learning to (Learn at Test Time): RNNs with Expressive Hidden States. arXiv 2024, arXiv:2407.04620. [Google Scholar]
- Beck, M.; Pöppel, K.; Spanring, M.; Auer, A.; Prudnikova, O.; Kopp, M.; Klambauer, G.; Brandstetter, J.; Hochreiter, S. xLSTM: Extended Long Short-Term Memory. arXiv 2024, arXiv:2405.04517. [Google Scholar]
- Smith, B.L.; Demetsky, M.J. Traffic flow forecasting: Comparison of modeling approaches. J. Transp. Eng. 1997, 123, 261–266. [Google Scholar] [CrossRef]
- Gardner, E.S., Jr. Exponential smoothing: The state of the art. J. Forecast. 1985, 4, 1–28. [Google Scholar] [CrossRef]
- Hamed, M.M.; Al-Masaeid, H.R.; Said, Z.M.B. Short-term prediction of traffic volume in urban arterials. J. Transp. Eng. 1995, 121, 249–254. [Google Scholar] [CrossRef]
- Kamarianakis, Y.; Prastacos, P. Space–time modeling of traffic flow. Comput. Geosci. 2005, 31, 119–133. [Google Scholar] [CrossRef]
- Li, X.; Pan, G.; Wu, Z.; Qi, G.; Li, S.; Zhang, D.; Zhang, W.; Wang, Z. Prediction of urban human mobility using large-scale taxi traces and its applications. Front. Comput. Sci. 2012, 6, 111–121. [Google Scholar] [CrossRef]
- Moreira-Matias, L.; Gama, J.; Ferreira, M.; Mendes-Moreira, J.; Damas, L. Predicting taxi–passenger demand using streaming data. IEEE Trans. Intell. Transp. Syst. 2013, 14, 1393–1402. [Google Scholar] [CrossRef]
- Lippi, M.; Bertini, M.; Frasconi, P. Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning. IEEE Trans. Intell. Transp. Syst. 2013, 14, 871–882. [Google Scholar] [CrossRef]
- Wagner-Muns, I.M.; Guardiola, I.G.; Samaranayke, V.; Kayani, W.I. A functional data analysis approach to traffic volume forecasting. IEEE Trans. Intell. Transp. Syst. 2017, 19, 878–888. [Google Scholar] [CrossRef]
- Wang, H.; Zhang, B. Research on ARIMA Model for Short-Term Traffic Flow Prediction based on Time Series. In Proceedings of the 2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan, 23–25 November 2023; pp. 92–95. [Google Scholar]
- Ahmed, M.S.; Cook, A.R. Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques; Transportation Research Board: Washington, DC, USA, 1979. [Google Scholar]
- Williams, B.M.; Hoel, L.A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. J. Transp. Eng. 2003, 129, 664–672. [Google Scholar] [CrossRef]
- Guan, J.; Wang, W.; Li, W.; Zhou, S. A unified framework for predicting kpis of on-demand transport services. IEEE Access 2018, 6, 32005–32014. [Google Scholar] [CrossRef]
- Zheng, L.; Zhu, C.; Zhu, N.; He, T.; Dong, N.; Huang, H. Feature selection-based approach for urban short-term travel speed prediction. IET Intell. Transp. Syst. 2018, 12, 474–484. [Google Scholar] [CrossRef]
- Diao, Z.; Zhang, D.; Wang, X.; Xie, K.; He, S.; Lu, X.; Li, Y. A hybrid model for short-term traffic volume prediction in massive transportation systems. IEEE Trans. Intell. Transp. Syst. 2018, 20, 935–946. [Google Scholar] [CrossRef]
- Salinas, D.; Bohlke-Schneider, M.; Callot, L.; Medico, R.; Gasthaus, J. High-dimensional multivariate forecasting with low-rank gaussian copula processes. In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems, NeurIPS 2019, Vancouver, BC, Canada, 8–14 December 2019. [Google Scholar]
- Sun, S.; Xu, X. Variational inference for infinite mixtures of Gaussian processes with applications to traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 2010, 12, 466–475. [Google Scholar] [CrossRef]
- Zhao, J.; Sun, S. High-order Gaussian process dynamical models for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 2016, 17, 2014–2019. [Google Scholar] [CrossRef]
- Gong, Y.; Li, Z.; Zhang, J.; Liu, W.; Zheng, Y.; Kirsch, C. Network-wide crowd flow prediction of sydney trains via customized online non-negative matrix factorization. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 22–26 October 2018; pp. 1243–1252. [Google Scholar]
- Shin, J.; Sunwoo, M. Vehicle speed prediction using a Markov chain with speed constraints. IEEE Trans. Intell. Transp. Syst. 2018, 20, 3201–3211. [Google Scholar] [CrossRef]
- Zhu, G.; Song, K.; Zhang, P.; Wang, L. A traffic flow state transition model for urban road network based on Hidden Markov Model. Neurocomputing 2016, 214, 567–574. [Google Scholar] [CrossRef]
- Shahi, T.B.; Shrestha, A.; Neupane, A.; Guo, W. Stock price forecasting with deep learning: A comparative study. Mathematics 2020, 8, 1441. [Google Scholar] [CrossRef]
- Shahi, T.B.; Sitaula, C. Natural language processing for Nepali text: A review. Artif. Intell. Rev. 2022, 55, 3401–3429. [Google Scholar] [CrossRef]
- Innamaa, S. Short-term prediction of traffic situation using MLP-neural networks. In Proceedings of the 7th World Congress on Intelligent Transport Systems, Turin, Italy, 6–9 November 2000; pp. 6–9. [Google Scholar]
- Taylor, C.; Meldrum, D. Freeway traffic data prediction using neural networks. In Proceedings of the Pacific Rim TransTech Conference. 1995 Vehicle Navigation and Information Systems Conference Proceedings. 6th International VNIS. A Ride into the Future, Seattle, WA, USA, 30 July–2 August 1995; pp. 225–230. [Google Scholar]
- Ledoux, C. An urban traffic flow model integrating neural networks. Transp. Res. Part C Emerg. Technol. 1997, 5, 287–300. [Google Scholar] [CrossRef]
- Ma, X.; Tao, Z.; Wang, Y.; Yu, H.; Wang, Y. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C Emerg. Technol. 2015, 54, 187–197. [Google Scholar] [CrossRef]
- Tian, Y.; Pan, L. Predicting short-term traffic flow by long short-term memory recurrent neural network. In Proceedings of the 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, China, 19–21 December 2015; pp. 153–158. [Google Scholar]
- Fu, R.; Zhang, Z.; Li, L. Using LSTM and GRU neural network methods for traffic flow prediction. In Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China, 11–13 November 2016; pp. 324–328. [Google Scholar]
- Liu, Y.; Zheng, H.; Feng, X.; Chen, Z. Short-term traffic flow prediction with Conv-LSTM. In Proceedings of the 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, 11–13 October 2017; pp. 1–6. [Google Scholar]
- Zhao, Z.; Chen, W.; Wu, X.; Chen, P.C.; Liu, J. LSTM network: A deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 2017, 11, 68–75. [Google Scholar] [CrossRef]
- Ma, X.; Dai, Z.; He, Z.; Ma, J.; Wang, Y.; Wang, Y. Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors 2017, 17, 818. [Google Scholar] [CrossRef]
- Song, C.; Lee, H.; Kang, C.; Lee, W.; Kim, Y.B.; Cha, S.W. Traffic speed prediction under weekday using convolutional neural networks concepts. In Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, 11–14 June 2017; pp. 1293–1298. [Google Scholar]
- Lea, C.; Vidal, R.; Reiter, A.; Hager, G.D. Temporal convolutional networks: A unified approach to action segmentation. In Proceedings of the Computer Vision–ECCV 2016 Workshops, Amsterdam, The Netherlands, 8–10 and 15–16 October 2016; Proceedings, Part III 14. pp. 47–54. [Google Scholar]
- Zhao, W.; Gao, Y.; Ji, T.; Wan, X.; Ye, F.; Bai, G. Deep temporal convolutional networks for short-term traffic flow forecasting. IEEE Access 2019, 7, 114496–114507. [Google Scholar] [CrossRef]
- Bi, J.; Zhang, X.; Yuan, H.; Zhang, J.; Zhou, M. A hybrid prediction method for realistic network traffic with temporal convolutional network and LSTM. IEEE Trans. Autom. Sci. Eng. 2021, 19, 1869–1879. [Google Scholar] [CrossRef]
- Ren, Y.; Zhao, D.; Luo, D.; Ma, H.; Duan, P. Global-local temporal convolutional network for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 2020, 23, 1578–1584. [Google Scholar] [CrossRef]
- Li, Y.; Yu, R.; Shahabi, C.; Liu, Y. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv 2017, arXiv:1707.01926. [Google Scholar]
- Yu, B.; Yin, H.; Zhu, Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv 2017, arXiv:1709.04875. [Google Scholar]
- Guo, S.; Lin, Y.; Feng, N.; Song, C.; Wan, H. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 27 January–1 February 2019; pp. 922–929. [Google Scholar]
- Zhao, L.; Song, Y.; Zhang, C.; Liu, Y.; Wang, P.; Lin, T.; Deng, M.; Li, H. T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 2019, 21, 3848–3858. [Google Scholar] [CrossRef]
- Wu, Z.; Pan, S.; Long, G.; Jiang, J.; Zhang, C. Graph wavenet for deep spatial-temporal graph modeling. arXiv 2019, arXiv:1906.00121. [Google Scholar]
- Lv, M.; Hong, Z.; Chen, L.; Chen, T.; Zhu, T.; Ji, S. Temporal multi-graph convolutional network for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 2020, 22, 3337–3348. [Google Scholar] [CrossRef]
- Lan, S.; Ma, Y.; Huang, W.; Wang, W.; Yang, H.; Li, P. Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In Proceedings of the International Conference on Machine Learning, Baltimore, MD, USA, 17–23 July 2022; pp. 11906–11917. [Google Scholar]
- Ashish, V. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017, 30, 1. [Google Scholar]
- Cai, L.; Janowicz, K.; Mai, G.; Yan, B.; Zhu, R. Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting. Trans. GIS 2020, 24, 736–755. [Google Scholar] [CrossRef]
- Zhou, H.; Zhang, S.; Peng, J.; Zhang, S.; Li, J.; Xiong, H.; Zhang, W. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2–9 February 2021; pp. 11106–11115. [Google Scholar]
- Jiang, J.; Han, C.; Zhao, W.X.; Wang, J. Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA, 7–14 February 2023; pp. 4365–4373. [Google Scholar]
- Rawat, D.; Singh, V.; Dhondiyal, S.A.; Singh, S. Time Series Forecasting Models: A Comprehensive. Available online: https://www.researchgate.net/publication/373439013_Time_Series_Forecasting_Models_A_Comprehensive_Review (accessed on 4 September 2024).
- Tokuyama, Y.; Fukushima, Y.; Yokohira, T. The effect of using attribute information in network traffic prediction with deep learning. In Proceedings of the 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea, 17–19 October 2018; pp. 521–525. [Google Scholar]
- Qu, L.; Lyu, J.; Li, W.; Ma, D.; Fan, H. Features injected recurrent neural networks for short-term traffic speed prediction. Neurocomputing 2021, 451, 290–304. [Google Scholar] [CrossRef]
- Wang, X.; Xu, L.; Chen, K. Data-driven short-term forecasting for urban road network traffic based on data processing and LSTM-RNN. Arab. J. Sci. Eng. 2019, 44, 3043–3060. [Google Scholar]
- Awan, F.M.; Minerva, R.; Crespi, N. Using noise pollution data for traffic prediction in smart cities: Experiments based on LSTM recurrent neural networks. IEEE Sens. J. 2021, 21, 20722–20729. [Google Scholar] [CrossRef]
- Zhang, J.; Zheng, Y.; Qi, D. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 4–9 February 2017. [Google Scholar]
- Yao, H.; Wu, F.; Ke, J.; Tang, X.; Jia, Y.; Lu, S.; Gong, P.; Ye, J.; Li, Z. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LO, USA, 2–7 February 2018. [Google Scholar]
- Bao, J.; Yu, H.; Wu, J. Short-term FFBS demand prediction with multi-source data in a hybrid deep learning framework. IET Intell. Transp. Syst. 2019, 13, 1340–1347. [Google Scholar] [CrossRef]
- Zhou, X.; Shen, Y.; Zhu, Y.; Huang, L. Predicting multi-step citywide passenger demands using attention-based neural networks. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 5–9 February 2018; pp. 736–744. [Google Scholar]
- Roudbari, N.S.; Patterson, Z.; Eicker, U.; Poullis, C. Simpler is better: Multilevel abstraction with graph convolutional recurrent neural network cells for traffic prediction. In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, 4–7 December 2022; pp. 1–10. [Google Scholar]
- Lu, Z.; Lv, W.; Cao, Y.; Xie, Z.; Peng, H.; Du, B. LSTM variants meet graph neural networks for road speed prediction. Neurocomputing 2020, 400, 34–45. [Google Scholar] [CrossRef]
- Wang, H.; Zhang, R.; Cheng, X.; Yang, L. Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network. IEEE Trans. Intell. Transp. Syst. 2022, 23, 16137–16147. [Google Scholar] [CrossRef]
- James, J. Graph construction for traffic prediction: A data-driven approach. IEEE Trans. Intell. Transp. Syst. 2022, 23, 15015–15027. [Google Scholar]
- Gu, Y.; Deng, L. Stagcn: Spatial–temporal attention graph convolution network for traffic forecasting. Mathematics 2022, 10, 1599. [Google Scholar] [CrossRef]
- Sugiartawan, P.; Hartati, S. Time series data prediction using elman recurrent neural network on tourist visits in tanah lot tourism object. J. Sist. Inf. Dan Komput. Terap. Indones 2019, 1, 314–320. [Google Scholar] [CrossRef]
- Hussain, B.; Afzal, M.K.; Ahmad, S.; Mostafa, A.M. Intelligent traffic flow prediction using optimized GRU model. IEEE Access 2021, 9, 100736–100746. [Google Scholar] [CrossRef]
- Lu, S.; Zhang, Q.; Chen, G.; Seng, D. A combined method for short-term traffic flow prediction based on recurrent neural network. Alex. Eng. J. 2021, 60, 87–94. [Google Scholar] [CrossRef]
- Kashyap, A.A.; Raviraj, S.; Devarakonda, A.; Nayak, K.S.R.; Santhosh, K.V.; Bhat, S.J. Traffic flow prediction models—A review of deep learning techniques. Cogent Eng. 2022, 9, 2010510. [Google Scholar] [CrossRef]
- Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Gated feedback recurrent neural networks. In Proceedings of the International Conference on Machine Learning, Lille, France, 6–11 July 2015; pp. 2067–2075. [Google Scholar]
- Luo, X.; Li, D.; Yang, Y.; Zhang, S. Spatiotemporal traffic flow prediction with KNN and LSTM. J. Adv. Transp. 2019, 2019, 4145353. [Google Scholar] [CrossRef]
- Zhou, L.; Zhang, Q.; Yin, C.; Ye, W. Research on Short-term Traffic Flow Prediction Based on KNN-GRU. In Proceedings of the 2022 China Automation Congress (CAC), Xiamen, China, 25–27 November 2022; pp. 1924–1928. [Google Scholar]
- Tong, J.; Gu, X.; Zhang, M.; Wan, J.; Wang, J. Traffic flow prediction based on improved SVR for VANET. In Proceedings of the 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), Changsha, China, 26–28 March 2021; pp. 402–405. [Google Scholar]
- Cai, L.; Chen, Q.; Cai, W.; Xu, X.; Zhou, T.; Qin, J. SVRGSA: A hybrid learning based model for short-term traffic flow forecasting. IET Intell. Transp. Syst. 2019, 13, 1348–1355. [Google Scholar] [CrossRef]
- Yu, H.; Wu, Z.; Wang, S.; Wang, Y.; Ma, X. Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 2017, 17, 1501. [Google Scholar] [CrossRef]
- Yao, H.; Tang, X.; Wei, H.; Zheng, G.; Li, Z. Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 27 January–1 February 2019; pp. 5668–5675. [Google Scholar]
- Wang, X.; Ma, Y.; Wang, Y.; Jin, W.; Wang, X.; Tang, J.; Jia, C.; Yu, J. Traffic flow prediction via spatial temporal graph neural network. In Proceedings of the Web Conference, Taipei, Taiwan, 20–24 April 2020; pp. 1082–1092. [Google Scholar]
- Bogaerts, T.; Masegosa, A.D.; Angarita-Zapata, J.S.; Onieva, E.; Hellinckx, P. A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data. Transp. Res. Part C Emerg. Technol. 2020, 112, 62–77. [Google Scholar] [CrossRef]
- Qin, G.; Niu, X.; Wang, J.; Qian, Q. Traffic flow prediction based on JANet and attention mechanism. In Proceedings of the 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi’an, China, 9–11 April 2021; pp. 131–134. [Google Scholar]
- Hu, H.; Lin, Z.; Hu, Q.; Zhang, Y. Attention mechanism with spatial-temporal joint model for traffic flow speed prediction. IEEE Trans. Intell. Transp. Syst. 2021, 23, 16612–16621. [Google Scholar] [CrossRef]
- Yu, J.; Wei, H.; Guo, H.; Cai, Y. Urban traffic state prediction based on SA-LSTM. IOP Conf. Ser. Earth Environ. Sci. 2021, 783, 12153. [Google Scholar] [CrossRef]
- Zhao, W.; Zhang, S.; Wang, B.; Zhou, B. Dstgrn: Traffic flow prediction via dynamic spatio-temporal graph recurrent network. In Proceedings of the 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China, 6–8 January 2023; pp. 599–604. [Google Scholar]
- Tian, X.; Du, L.; Zhang, X.; Wu, S. Mat-wgcn: Traffic speed prediction using multi-head attention mechanism and weighted adjacency matrix. Sustainability 2023, 15, 13080. [Google Scholar] [CrossRef]
- Zheng, H.; Lin, F.; Feng, X.; Chen, Y. A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 2020, 22, 6910–6920. [Google Scholar] [CrossRef]
- Shu, W.; Cai, K.; Xiong, N.N. A short-term traffic flow prediction model based on an improved gate recurrent unit neural network. IEEE Trans. Intell. Transp. Syst. 2021, 23, 16654–16665. [Google Scholar] [CrossRef]
- Chen, L.; Shao, W.; Lv, M.; Chen, W.; Zhang, Y.; Yang, C. AARGNN: An attentive attributed recurrent graph neural network for traffic flow prediction considering multiple dynamic factors. IEEE Trans. Intell. Transp. Syst. 2022, 23, 17201–17211. [Google Scholar] [CrossRef]
- Huang, X.; Jiang, Y.; Tang, J. MAPredRNN: Multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion. Appl. Intell. 2023, 53, 19372–19383. [Google Scholar] [CrossRef]
- Liu, S.; Li, Z.; Li, H. Research on short-term traffic flow prediction model based on RNN-LSTM. IOP Conf. Ser. Mater. Sci. Eng. 2020, 806, 12017. [Google Scholar] [CrossRef]
- Sengupta, A.; Das, A.; Guler, S.I. Hybrid hidden Markov LSTM for short-term traffic flow prediction. arXiv 2023, arXiv:2307.04954. [Google Scholar]
- Wang, Z.; Han, W. Traffic Flow Prediction Based on Optimized LSTM Model. In Proceedings of the 2023 3rd International Conference on Information Communication and Software Engineering (ICICSE), Chongqing, China, 7–9 April 2023; pp. 60–65. [Google Scholar]
- Zhou, Y.; He, J.; Wu, Z. Improved LSTM model for short-term traffic flow prediction with weather consideration. In Proceedings of the Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), Hangzhou, China, 19–21 May 2023; pp. 1546–1554. [Google Scholar]
- Yang, Z.; Wang, C. Short-term traffic flow prediction based on AST-MTL-CNN-GRU. IET Intell. Transp. Syst. 2023, 17, 2205–2220. [Google Scholar] [CrossRef]
- Lin, G.; Ding, J.; Ding, S.; Huang, B.; Yin, Y.; Zhang, K.; Li, Z. Passenger Flow Prediction with Transformer: The Shenzhen Metro Case. In Proceedings of the 2021 International Conference on Big Data Analysis and Computer Science (BDACS), Kunming, China, 25–27 June 2021; pp. 97–100. [Google Scholar]
- El Maazouzi, Q.; Retbi, A.; Bennani, S. Automatisation hyperparameters tuning process for times series forecasting: Application to passenger’s flow prediction on a railway network. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2022, 48, 53–60. [Google Scholar] [CrossRef]
- Izudheen, S. Short-term passenger count prediction for metro stations using LSTM network. Turk. J. Comput. Math. Educ. (TURCOMAT) 2021, 12, 4026–4034. [Google Scholar]
- Wen, X.-P. Single-site passenger flow forecast based on ga-lstm. In Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence, Virtual, 11–13 March 2022; pp. 16–20. [Google Scholar]
- Zhai, X.; Shen, Y. Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network. Appl. Sci. 2023, 13, 4910. [Google Scholar] [CrossRef]
- Zeng, L.; Li, Z.; Yang, J.; Xu, X. Research on short-term passenger flow prediction method of urban rail transit based on CEEMDAN-IPSO-LSTM. J. Railw. Sci. Eng. 2023, 12, 1–14. [Google Scholar]
- Xu, Z.; Hou, L.; Zhang, Y.; Zhang, J. Passenger Flow Prediction of Scenic Spot Using a GCN–RNN Model. Sustainability 2022, 14, 3295. [Google Scholar] [CrossRef]
- Wang, X.; Bai, W.; Meng, Z.; Xin, B.; Gao, R.; Lv, X. The Prediction of Flow in Railway Station Based on RRC-STGCN. IEEE Access 2023, 11, 131128–131139. [Google Scholar] [CrossRef]
- ul Islam, B.; Ahmed, S.F. Research Article Short-Term Electrical Load Demand Forecasting Based on LSTM and RNN Deep Neural Networks. Math. Probl. Eng. 2022, 2022, 2316474. [Google Scholar]
- Toqué, F.; Côme, E.; El Mahrsi, M.K.; Oukhellou, L. Forecasting dynamic public transport origin-destination matrices with long-short term memory recurrent neural networks. In Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 1–4 November 2016; pp. 1071–1076. [Google Scholar]
- Nejadettehad, A.; Mahini, H.; Bahrak, B. Short-term demand forecasting for online car-hailing services using recurrent neural networks. Appl. Artif. Intell. 2020, 34, 674–689. [Google Scholar] [CrossRef]
- Feng, S.; Ke, J.; Yang, H.; Ye, J. A multi-task matrix factorized graph neural network for co-prediction of zone-based and OD-based ride-hailing demand. IEEE Trans. Intell. Transp. Syst. 2021, 23, 5704–5716. [Google Scholar] [CrossRef]
- Wang, N.; Zheng, L.; Shen, H.; Li, S. Ride-hailing origin-destination demand prediction with spatiotemporal information fusion. Transp. Saf. Environ. 2024, 6, tdad026. [Google Scholar] [CrossRef]
- Gu, M.; Duan, Z.; Daily, O.D. Demand Prediction in Urban Metro Transit System: A Convolutional LSTM Neural Network with Multi-factor Fusion Channel-wise Attention. In Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, 8–12 October 2022; pp. 3398–3404. [Google Scholar]
- Kim, Y.; Wang, P.; Mihaylova, L. Structural recurrent neural network for traffic speed prediction. In Proceedings of the ICASSP 2019—2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 12–17 May 2019; pp. 5207–5211. [Google Scholar]
- Cui, Z.; Ke, R.; Pu, Z.; Wang, Y. Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv 2018, arXiv:1801.02143. [Google Scholar]
- Lv, Z.; Xu, J.; Zheng, K.; Yin, H.; Zhao, P.; Zhou, X. Lc-rnn: A deep learning model for traffic speed prediction. In Proceedings of the IJCAI, 2018 International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 13–19 July 2018; p. 27. [Google Scholar]
- Ma, C.; Zhao, Y.; Dai, G.; Xu, X.; Wong, S.-C. A novel STFSA-CNN-GRU hybrid model for short-term traffic speed prediction. IEEE Trans. Intell. Transp. Syst. 2022, 24, 3728–3737. [Google Scholar] [CrossRef]
- Abdelraouf, A.; Abdel-Aty, M.; Yuan, J. Utilizing attention-based multi-encoder-decoder neural networks for freeway traffic speed prediction. IEEE Trans. Intell. Transp. Syst. 2021, 23, 11960–11969. [Google Scholar] [CrossRef]
- Hu, X.; Liu, T.; Hao, X.; Lin, C. Attention-based Conv-LSTM and Bi-LSTM networks for large-scale traffic speed prediction. J. Supercomput. 2022, 78, 12686–12709. [Google Scholar] [CrossRef]
- Yin, S.; Wang, J.; Cui, Z.; Wang, Y. Attention-enabled network-level traffic speed prediction. In Proceedings of the 2020 IEEE International Smart Cities Conference (ISC2), Virtual, 28 September–1 October 2020; pp. 1–8. [Google Scholar]
- Duan, Y.; Yisheng, L.; Wang, F.-Y. Travel time prediction with LSTM neural network. In Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 1–4 November 2016; pp. 1053–1058. [Google Scholar]
- Yuan, Y.; Shao, C.; Cao, Z.; He, Z.; Zhu, C.; Wang, Y.; Jang, V. Bus dynamic travel time prediction: Using a deep feature extraction framework based on RNN and DNN. Electronics 2020, 9, 1876. [Google Scholar] [CrossRef]
- Ran, X.; Shan, Z.; Fang, Y.; Lin, C. An LSTM-based method with attention mechanism for travel time prediction. Sensors 2019, 19, 861. [Google Scholar] [CrossRef] [PubMed]
- Petersen, N.C.; Rodrigues, F.; Pereira, F.C. Multi-output bus travel time prediction with convolutional LSTM neural network. Expert Syst. Appl. 2019, 120, 426–435. [Google Scholar] [CrossRef]
- Ting, P.-Y.; Wada, T.; Chiu, Y.-L.; Sun, M.-T.; Sakai, K.; Ku, W.-S.; Jeng, A.A.-K.; Hwu, J.-S. Freeway travel time prediction using deep hybrid model–taking Sun Yat-Sen freeway as an example. IEEE Trans. Veh. Technol. 2020, 69, 8257–8266. [Google Scholar] [CrossRef]
- Katayama, H.; Yasuda, S.; Fuse, T. Traffic density based travel-time prediction with GCN-LSTM. In Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, 8–12 October 2022; pp. 2908–2913. [Google Scholar]
- Shen, Y.; Jin, C.; Hua, J.; Huang, D. TTPNet: A neural network for travel time prediction based on tensor decomposition and graph embedding. IEEE Trans. Knowl. Data Eng. 2020, 34, 4514–4526. [Google Scholar] [CrossRef]
- Zhang, T.T.; Ye, Y. Big Data Analytics for Network Level Short-Term Travel Time Prediction with Hierarchical LSTM and Attention. arXiv 2022, arXiv:2201.05760. [Google Scholar]
- Chughtai, J.-U.-R.; Haq, I.U.; Muneeb, M. An attention-based recurrent learning model for short-term travel time prediction. PLoS ONE 2022, 17, e0278064. [Google Scholar] [CrossRef] [PubMed]
- Sameen, M.I.; Pradhan, B. Severity prediction of traffic accidents with recurrent neural networks. Appl. Sci. 2017, 7, 476. [Google Scholar] [CrossRef]
- Pradhan, B.; Ibrahim Sameen, M.; Pradhan, B.; Ibrahim Sameen, M. Applications of deep learning in severity prediction of traffic accidents. In Laser Scanning Systems in Highway and Safety Assessment; Advances in Science, Technology & Innovation; Springer: Cham, Switzerland, 2020; pp. 129–139. [Google Scholar]
- Chui, K.T.; Gupta, B.B.; Liu, R.W.; Zhang, X.; Vasant, P.; Thomas, J.J. Extended-range prediction model using NSGA-III optimized RNN-GRU-LSTM for driver stress and drowsiness. Sensors 2021, 21, 6412. [Google Scholar] [CrossRef] [PubMed]
- Wang, S.; Yan, C.; Shao, Y. Road Traffic Accident Prediction Model Based on J-LSTM+ Attention Mechanism. In Proceedings of the 2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, 26–29 May 2023; pp. 635–638. [Google Scholar]
- Yu, L.; Du, B.; Hu, X.; Sun, L.; Han, L.; Lv, W. Deep spatio-temporal graph convolutional network for traffic accident prediction. Neurocomputing 2021, 423, 135–147. [Google Scholar] [CrossRef]
- Akhtar, M.; Moridpour, S. A review of traffic congestion prediction using artificial intelligence. J. Adv. Transp. 2021, 2021, 8878011. [Google Scholar] [CrossRef]
- Shin, D.-H.; Chung, K.; Park, R.C. Prediction of traffic congestion based on LSTM through correction of missing temporal and spatial data. IEEE Access 2020, 8, 150784–150796. [Google Scholar] [CrossRef]
- Ranjan, N.; Bhandari, S.; Zhao, H.P.; Kim, H.; Khan, P. City-wide traffic congestion prediction based on CNN, LSTM and transpose CNN. IEEE Access 2020, 8, 81606–81620. [Google Scholar] [CrossRef]
- Jin, G.; Liu, L.; Li, F.; Huang, J. Spatio-temporal graph neural point process for traffic congestion event prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA, 7–14 February 2023; pp. 14268–14276. [Google Scholar]
- Kim, B.; Kang, C.M.; Kim, J.; Lee, S.H.; Chung, C.C.; Choi, J.W. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. In Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, 16–19 October 2017; pp. 399–404. [Google Scholar]
- Jose, A.; Vidya, V. A stacked long short-term memory neural networks for parking occupancy rate prediction. In Proceedings of the 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India, 18–19 June 2021; pp. 522–525. [Google Scholar]
- Zeng, C.; Ma, C.; Wang, K.; Cui, Z. Parking occupancy prediction method based on multi factors and stacked GRU-LSTM. IEEE Access 2022, 10, 47361–47370. [Google Scholar] [CrossRef]
- Ma, T.-Y.; Faye, S. Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks. Energy 2022, 244, 123217. [Google Scholar] [CrossRef]
- Reza, S.; Ferreira, M.C.; Machado, J.J.; Tavares, J.M.R. A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks. Expert Syst. Appl. 2022, 202, 117275. [Google Scholar] [CrossRef]
- Liu, Q.; Li, J.; Lu, Z. ST-Tran: Spatial-temporal transformer for cellular traffic prediction. IEEE Commun. Lett. 2021, 25, 3325–3329. [Google Scholar] [CrossRef]
- Luo, Q.; He, S.; Han, X.; Wang, Y.; Li, H. LSTTN: A Long-Short Term Transformer-based spatiotemporal neural network for traffic flow forecasting. Knowl.-Based Syst. 2024, 293, 111637. [Google Scholar] [CrossRef]
- Lin, S.; Lin, W.; Wu, W.; Zhao, F.; Mo, R.; Zhang, H. Segrnn: Segment recurrent neural network for long-term time series forecasting. arXiv 2023, arXiv:2308.11200. [Google Scholar]
- Zeng, A.; Chen, M.; Zhang, L.; Xu, Q. Are transformers effective for time series forecasting? In Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA, 7–14 February 2023; pp. 11121–11128. [Google Scholar] [CrossRef]
- Liu, M.; Zeng, A.; Chen, M.; Xu, Z.; Lai, Q.; Ma, L.; Xu, Q. Scinet: Time series modeling and forecasting with sample convolution and interaction. Adv. Neural Inf. Process. Syst. 2022, 35, 5816–5828. [Google Scholar]
- Liu, Y.; Wang, Y.; Yang, X.; Zhang, L. Short-term travel time prediction by deep learning: A comparison of different LSTM-DNN models. In Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, 16–19 October 2017; pp. 1–8. [Google Scholar]
Model | Time Steps | RMSE | MAE |
---|---|---|---|
ARIMA | 1 | 34.18 | 17.71 |
3 | 47.21 | 23.10 | |
6 | 66.60 | 30.94 | |
SVR | 1 | 30.22 | 15.33 |
3 | 37.28 | 17.84 | |
6 | 46.17 | 20.97 | |
Informer | 1 | 27.48 | 14.96 |
3 | 28.35 | 15.22 | |
6 | 28.55 | 15.25 | |
LSTM | 1 | 21.91 | 11.66 |
3 | 23.78 | 12.47 | |
6 | 24.55 | 12.84 |
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He, Y.; Huang, P.; Hong, W.; Luo, Q.; Li, L.; Tsui, K.-L. In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review. Algorithms 2024, 17, 398. https://doi.org/10.3390/a17090398
He Y, Huang P, Hong W, Luo Q, Li L, Tsui K-L. In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review. Algorithms. 2024; 17(9):398. https://doi.org/10.3390/a17090398
Chicago/Turabian StyleHe, Yuxin, Ping Huang, Weihang Hong, Qin Luo, Lishuai Li, and Kwok-Leung Tsui. 2024. "In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review" Algorithms 17, no. 9: 398. https://doi.org/10.3390/a17090398