A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

Traffic flow prediction remains a significant challenge in intelligent transportation systems. To enhance prediction accuracy, a hybrid model is proposed that integrates an improved Variational Mode Decomposition (VMD), a Residual Spatio-Temporal Graph Convolutional Network (RSTGCN), a Long-term Time Series Informer Network (LTSIN), and an iterative error compensation strategy. This approach effectively addresses the non-stationarity, spatial correlations, and temporal dependencies inherent in traffic data. First, the VMD is optimized using a Crested Porcupine Optimizer algorithm to decompose traffic flow time series into intrinsic mode functions (IMFs), each capturing different traffic patterns. Next, these IMFs are then input into the LTSIN and RSTGCN modules. The LTSIN models long-term temporal dependencies, while the RSTGCN captures short-term temporal and spatial relationships. Residual connectivity within the RSTGCN allows the model to retain node features and aggregate information from neighboring nodes, mitigating the over-smoothing issue typically seen in graph convolutional networks. Lastly, an iterative error correction mechanism is applied, where the IMF-derived predictions are refined and reconstructed to yield the final forecast. The effectiveness of the proposed model is demonstrated by extensive experiments, including ablation experiments, baseline comparisons, and multi-step forecasting experiments. Compared to existing models such as GCN and Informer, the proposed method reduces MAE by 8.78 and 6.79, and MAPE by 6.48% and 5.52%, respectively. These results indicate that the proposed model achieves superior prediction accuracy and can be effectively applied to traffic flow prediction tasks.

Short term traffic flow prediction of expressway service area based on STL-OMS

With the acceleration of urbanization and the continuous growth of transportation demand, traffic flow prediction has become a key research issue in ITS (Intelligent Transportation System). In this paper, a traffic flow prediction model based on VMD-ARO-BiLSTM is proposed, which can make full use of the spatio-temporal characteristics of traffic data to achieve high-precision traffic flow prediction. First, the traffic flow data are input into VMD (Variational Mode Decomposition) for data reconstruction. Then, the optimal parameter values of BiLSTM (Bi-directional Long Short-Term Memory) are solved using ARO (Artificial Rabbits Optimization) to complete the optimization of BiLSTM. Finally, the reconstructed data are input into BiLSTM to realize the accurate prediction of traffic flow. To verify the model performance, we conducted experiments using traffic flow data from different time periods. The experimental results show that on weekdays, the combined VMD-ARO-BiLSTM prediction model decreases the RMSE (Root Mean Square Error) by 47.2% and the MAE (Mean Absolute Error) by 44.6% compared to BiLSTM; on weekends, compared to BiLSTM, the RMSE decreases by 26.1% and MAE decreases by 23.4%, reflecting the strong robustness and applicability of this model, which has certain application value.

Short-term traffic flow prediction plays an important role in intelligent transportation system. Numerous researchers have paid much attention to it in the past decades. However, the performance of traditional traffic flow prediction methods is not satisfactory, for those methods cannot describe the complicated nonlinearity and uncertainty of the traffic flow precisely. Neural networks were used to deal with the issues, but most of them failed to capture the deep features of traffic flow and be sensitive enough to the time-aware traffic flow data. In this paper, we propose a deep bi-directional long short-term memory (DBL) model by introducing long short-term memory (LSTM) recurrent neural network, residual connections, deeply hierarchical networks and bi-directional traffic flow. The proposed model is able to capture the deep features of traffic flow and take full advantage of time-aware traffic flow data. Additionally, we introduce the DBL model, regression layer and dropout training method into a traffic flow prediction architecture. We evaluate the prediction architecture on the dataset from Caltrans Performance Measurement System (PeMS). The experiment results demonstrate that the proposed model for short-term traffic flow prediction obtains high accuracy and generalizes well compared with other models.

Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of traffic flow prediction, we developed a traffic flow prediction framework—namely, traffic flow multicomponent network—that appropriately processes the noise, volatility, and nonlinearity in traffic flow data. This framework comprises three components: a factor selection component, traffic flow decomposition component, and traffic flow prediction component. The factor selection component considers the dynamic effects of weather‐related, environmental, and spatiotemporal factors on traffic flow; it then extracts and analyzes factors exhibiting strong correlations with traffic flow. The traffic flow decomposition component optimizes the parameters of variational mode decomposition on the basis of the envelope entropy by using the sparrow search algorithm; it then transforms traffic flow into multiple intrinsic mode functions to enable accurate traffic flow prediction. Finally, the traffic flow prediction component constructs dynamic feature matrices by using a bidirectional gated recurrent unit model to identify relationships within the data. Moreover, it uses an attention mechanism to assign different weights to different features on the basis of the importance of these features to traffic flow prediction, thereby enabling the efficient processing of a large volume of data. The performance of the proposed framework was examined in experiments conducted on large volumes of traffic flow data with different time granularities. The results indicated that the proposed framework achieved high prediction accuracy and stability for various time granularities, data samples, dataset sizes, and noise conditions. Moreover, it generally outperformed existing traffic flow prediction models under all experimental conditions.

To address the issue of spatiotemporal illusion in short-term traffic flow prediction and deeply explore the underlying short-term traffic flow network characteristics, a traffic flow prediction model that combines long-term spatiotemporal heterogeneity with short-term spatiotemporal features is proposed. In the long-term spatiotemporal branch, the Transformer structure is employed, and a self-supervised masking mechanism is utilized to pretrain the heterogeneity in long-term temporal and spatial dimensions separately. Additionally, a spatiotemporal adaptive module is designed, which adapts to and guides short-term traffic flow prediction across time series and traffic flow networks. In the short-term spatiotemporal branch, a recurrent neural ordinary differential equation (ODE) module is devised. This module is capable of continuously and dynamically adjusting short-term spatiotemporal features, better capturing and exploring potential short-term spatiotemporal characteristics. Through multiple cycles, this module gradually and accurately extracts and compresses road network features, integrates the adapted spatiotemporal heterogeneity, and reconstructs future short-term traffic flows in the decoder. Experiments are conducted on four traffic flow and two traffic speed datasets, showing that compared to traditional time series models, the proposed model’s prediction accuracy indicators have relatively improved by 45.09%, 39.14%, and 0.47% on average; compared to recurrent neural network (RNN) series models, the improvements are 18.91%, 15.77%, and 0.18% on average; compared to graph convolution series models, the improvements are 21.31%, 16.65%, and 0.21% on average; and compared to Transformer series models, the improvements are 6.57%, 6.23%, and 0.05% on average. The model’s general applicability and good performance in transportation speed scenarios were verified through a multi-step experiment conducted on the transportation speed dataset.

Accurate long-term traffic prediction is crucial for enhancing traffic efficiency, ensuring urban safety, and fostering sustainable urban development. However, forecasting over extended periods is challenging due to intricate trends, cyclical variations, and interference from outlier data. To address these issues, this study proposes a matrix-based traffic flow prediction model. The model constructs a matrix with periods as rows and similarities as columns, leveraging periodicity and similarity in traffic data. A row-column prediction module links these patterns efficiently, while a fluctuation transformation mitigates the impact of outliers, significantly improving forecast accuracy. Extending the forecast time span to 14 days with hourly intervals, the model was validated using the PeMS dataset provided by the California Department of Transportation. Results demonstrate the model’s effectiveness in capturing complex temporal dynamics, providing a robust tool for long-term traffic prediction.

Accurate traffic flow prediction plays a vital role in intelligent transportation systems, helping traffic management departments maintain stable traffic order, reduce traffic congestion, and improve road safety. Existing prediction methods focus on dynamic modeling of the spatiotemporal dependencies of traffic flow, capturing the periodicity and spatial heterogeneity in traffic data. However, they still suffer from a lack of focus on the important local information in long-term predictions, leading to overly smooth results that fail to effectively capture sudden changes in traffic patterns. To address these limitations, we propose the sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network (sAMDGCN) model. Specifically, we extend sLSTM and introduce temporal trend-aware multi-head attention to jointly capture the complex temporal dependencies. We propose a multi-head dynamic graph convolutional network to capture a wider range of dynamic spatial dependencies. To validate the effectiveness of sAMDGCN, we perform extensive experiments on four real-world traffic flow datasets. Experimental results show that our proposed sAMDGCN model outperforms the advanced baseline methods in long-term traffic flow prediction tasks, demonstrating its superior performance in capturing complex and dynamic traffic patterns.

Nowadays accurate and efficient traffic flow prediction is strongly needed by individual travelers and public transport management. Traffic flow prediction, especially long-term prediction, plays an important role in the application of intelligent transportation systems (ITS). In this paper, we propose a personalized design model (ResDeconvNN) based on Convolutional Neural Network (CNN) for long-term traffic flow prediction of elevated highways in Shanghai. The next whole day flow information can be predicted using the previous day flows. Taking the correlation of traffic parameters into account, we analogy flow, speed and occupancy (FSO) to the 3 channels of RGB as the 3 inputs of model. So the raw data collected from loop detectors are transformed into a spatial-temporal matrix which has 3 channels. Our model consists of two modules: Residual net and deconvolutional neural network. First, we take advantage of the residual net in deep network to extract the features of traffic. Then, we develop a deconvolutional network module and apply it to decode the flow of the next day from the comprehensive spatial and temporal traffic features. Experimental results indicate that the proposed model is robust and can achieve a better prediction accuracy compared with the other existing popular approaches.

Traffic flow prediction is a key step to realizing the effective guidance and control of intelligent transportation systems. For using the short-term non-stationarity and spatio-temporal correlation presented in traffic flow, a spatio-temporal hybrid prediction model, which called ST-VGBiGRU, based on improved Variational Modal Decomposition (VMD), Graph Attention Network (GAT), and Bidirectional Gated Recurrent Unit (BiGRU) network is proposed. First, the traffic flow sequence is decomposed into a series of relatively stationary modal components using VMD algorithm to reduce its short-term non-stationarity. The high-frequency modal components are noise-reduced using the Fuzzy Entropy (Fuzzy En) method to improve the accuracy of decomposition. After that, the GAT network is used to capture the different attention levels of the prediction node to their neighboring traffic nodes, which obtain more spatial characteristics of traffic flow. Then, each modal component containing spatial features is fed into the BiGRU network separately to capture its temporal correlation. Each model parameter is trained to the optimum using the improved RMSProp algorithm, which improves the model’s prediction accuracy while speeding up the convergence of RMSProp algorithm. In order to illustrate the performance of the ST-VGBiGRU model, the RTMC traffic dataset is used to conduct ablation experiments on the improved VMD module, the GAT module, and the improved BiGRU module. Meanwhile, we combined the PeMS traffic dataset to conduct baseline experiments and multi-step prediction experiments with the other six models. The results show that the prediction performance of our model is better than all the other baseline models.

Short-term traffic flow prediction (STFP) is one of the key technologies in Intelligence Transportation System (ITS). With the development of artificial intelligence technology, deep learning has been employed to STFP and certain achievements have been obtained. However, further improvement of STFP accuracy is constrained by the presence of noise, modal aliasing in the modal components and insufficient feature extraction of neural networks. Therefore, to more accurately capture the changing trend of traffic flow in different time periods, and increase the reliability and interpretability of the prediction models, improved variational mode decomposition (IVMD) and self-attention mechanism (SAM) based hybrid convolutional neural network (CNN) and long short term memory network (LSTM) are proposed for STFP in this study, benefited from the modal decomposition of IVMD and redistribution of the neural weights in CNN-LSTM networks by SAM, the proposed IVMD-CNN-LSTM-SAM effectively suppresses the influence of randomness and volatility of traffic flow, and accurately predict the short-term lane occupancy in the designated area. Furthermore, this study analyses the mechanism of SAM and IVMD in STFP, and proves the superiority of IVMD and SAM in signal decomposition with physical significance and weight allocation respectively. Therefore, the interpretability of the proposed neural network model is significantly improved. Eleven competitive neural networks forecasting methods are used as benchmarks and numerical studies based on actual traffic flow validate the accuracy and stability of the proposed IVMD-CNN-LSTM-SAM. Mean absolute percentage error and goodness of fit of the proposed model are 0.81% and 0.9968, respectively.

Timely and accurate traffic flow information is essential for the realization of intelligent transportation systems (ITS). For the existing traffic flow prediction models, only the temporal features were extracted but the spatial features were neglects. A short-term traffic flow prediction model based on Pearson Correlation Coefficient (PCC) and Bi-directional Long Short-Term Memory Network (BiLSTM) model was proposed. The PCC was used to mine the spatial dependence of the road network, and the temporal features of traffic flow were mined using BiLSTM model. The extracted temporal and spatial features were combined to achieve short-term flow prediction. The experimental results show that the PCC-BiLSTM model has higher prediction accuracy and lower error than the prediction models considering only temporal features, which verifies the effectiveness of traffic flow prediction in consideration of both temporal and spatial characteristics.

This work proposes a novel uncertainty quantification framework for long-term traffic flow prediction (TFP) based on a sequential deep learning model. Quantifying the uncertainty of TFP is crucial for intelligent transportation system (ITS) to make robust traffic congestion analysis and efficient traffic management due to the inherent uncertain and fluctuating nature of traffic flow. However, the performance (e.g., reliability and sharpness) of uncertainty quantification is hard to guarantee, particularly for long-term traffic flow (e.g., one week or two weeks in advance). To this end, this work develops a nonparametric performance-oriented prediction interval (PI) construction approach based on an enhanced sequential convolutional long short-term memory units (ConvLSTM) model, which is named as PI-ConvLSTM. This model can well learn the temporal correlations involved in the multivariate explanatory samples. Specifically, a periodic pattern learning strategy and a performance-oriented loss function are developed to ensure the quality of the derived PIs. Through validating on the real-life England freeway traffic flow dataset, the proposed PI-ConvLSTM proves to be capable of producing the skillful PIs for long-term TFP. For instance, the performance of derived PIs for two-week ahead is 0.175%, 0.198 and 1957.127 in average in terms of reliability, average width and sharpness, respectively. As compared to the benchmark models the proposed model shows at least 68.1% improvement on reliability, 3.4% on average width and 1.7% on sharpness.

Based on historical data, a new short-term traffic flow prediction model (ICEEMDAN-MPE-PSO-DELM-ARIMA) for intersections is proposed. In order to improve prediction accuracy, ICEEMDAN decomposition algorithm is applied on traffic flow time series to obtain multiple Intrinsic Mode Function (IMF) components. Then PSO-MPE algorithm is introduced to obtain the multi-scale permutation entropy values of each IMF component, to judge the randomness. According to the randomness, different prediction models are established. Prediction models based on PSO-DELM algorithm are established for IMF components with big randomness. ARIMA prediction models are established for IMF components with small randomness. In order to obtain the final predicted traffic flow values, multiple prediction results are added together. Finally, two actual intersections are selected to verify the proposed model. Results show that compared with other models, the proposed model has the smallest prediction errors and the best fitting effect with the real values, which can effectively improve prediction accuracy.

Accurate short-term traffic flow prediction is the foundation of the efficient and proactive management of freeway networks, especially on the abnormal traffic states. The relationship between traffic flow on the current section and the upstream stations can be used for predicting short-term traffic flow. In this paper, we reveal this relationship by the traffic flow structure pattern. The structure pattern can be drawn from real freeway toll data and a few video detective cameras on the freeway segments. Based on the stability pattern, a new traffic flow prediction algorithm has been proposed. Experimental based on real data showed that the prediction method based on structure pattern is an effective approach for traffic flow prediction, especially on the abnormal traffic state.