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
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
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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
APA StyleHe, Y., Huang, P., Hong, W., Luo, Q., Li, L., & Tsui, K. -L. (2024). In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review. Algorithms, 17(9), 398. https://doi.org/10.3390/a17090398