Road traffic flow prediction based on dynamic spatiotemporal graph attention network

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.

Traffic flow prediction is an important part of intelligent transportation system (ITS). Accurate traffic flow prediction information can provide reliable reference for traffic management decision-makers, effectively reduce traffic congestion, reduce vehicle exhaust pollution, save energy and facilitate the travel of the people. Most of the existing traffic flow prediction models simplify the traffic flow change process into a linear and stable process and ignore the influence of weather conditions on traffic flow, which will lead to large prediction deviation. In order to improve the accuracy of traffic flow prediction, this paper proposes an ensemble learning method based on ET and AdaBoost for fusing traffic and meteorological data under the non-stationary environment to predict the changes of traffic flow. The experiment is based on real traffic and meteorological data, and the results show that the proposed ensemble method is superior to the baseline methods.

Traffic flow prediction accuracy is very important for intelligent transportation systems (ITS). Many studies have proposed different methods for traffic flow prediction including ARIMA, ANN and SVM. With the development of deep learning technology, the evolutionary models of RNN such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units) models have been found to perform well in traffic flow prediction. This paper aims at investigating the use of the Random Forest Regressor Model, an ensemble learning algorithm, for improved and accurate traffic prediction. Random Forest algorithm is highly robust and is well suited for large datasets with many features, thus making it suitable for traffic forecasting. In this research, historical traffic data is used to train the model together with other variables such as traffic flow, weather and time. The Random Forest model performance is compared with the traditional prediction methods using Mean Squared Error. It shows that the Random Forest model is better than the conventional methods and can give better accurate forecasts of traffic flow and can be used in real time traffic management. It presents the actual and predicted vehicle count per hour.

Real-time and accurate traffic flow prediction plays an important role in ITS (Intelligent Transport System). Extreme learning machine (ELM) has proven to be an efficient and effective learning paradigm for a wide field. With the method of kernel function instead of the hidden layer, Kernel-ELM overcame the problem of variation caused by randomly assigned weights. In order to improve the accuracy of traffic flow prediction, this paper introduces a kernel extreme learning machine(KELM)-based forecasting method. We have implemented KELM to forecast real-time traffic flow in the data of Nanning in South China. The results indicate that the KELM method can generate a more accurate prediction and provide better performance on extremely fast learning speed compared with other state-of-art algorithms.

Traffic flow prediction is fundamental to traffic information services, control, and guidance. The challenge to accurately model the traffic flow is to comprehensively capture dynamic, global spatial, and local temporal correlations. To address challenges in spatiotemporal dependencies, global similarity, and local dynamics, we propose a multilayer spatiotemporal correlation-aware graph attention network (MSTC-GAT) for traffic flow prediction. Our model contains a multilayer spatial structure-aware module [spatial graph attention network (S-GAT)] using a spatial GAT with hierarchical attention masks and a path-based node correlation matrix to effectively capture local and global spatial dependencies. The temporal structure-aware module [temporal graph attention networks (T-GATs)] constructs a short-term similarity matrix of nodes for the temporal GAT to capture local dynamic temporal dependencies. Finally, a spatiotemporal Transformer (ST-Transformer) fuses weighted spatiotemporal node embeddings to capture global dynamic dependencies for accurate prediction. We conduct extensive experiments on four public benchmark datasets compared with 10 state-of-the-art models. The experimental results demonstrate that the MSTC-GAT outperforms all comparisons for short- and long-term predictions.

Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding

Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction methods such as temporal graph convolutional networks(T-GCNs) ignore the dissimilarities between lanes. Thus, they cannot provide more specific information regarding predictions such as dynamic changes in traffic flow direction and deeper lane relationships. With the upgrading of intersection sensors, more and more intersection lanes are equipped with intersection sensors to detect vehicle information all day long. These spatio-temporal data help researchers refine the focus of traffic prediction research down to the lane level. More accurate and detailed data mean that it is more difficult to mine the spatio-temporal correlations between data, and modeling heterogeneous data becomes more challenging. In order to deal with these problems, we propose a heterogeneous graph convolution model based on dynamic graph generation. The model consists of three components. The internal graph convolution network captures the real-time spatial dependency between lanes in terms of generated dynamic graphs. The external heterogeneous data fusion network comprehensively considers other parameters such as lane speed, lane occupancy, and weather conditions. The codec neural network utilizes a temporal attention mechanism to capture the deep temporal dependency. We test the performance of this model based on two real-world datasets, and extensive comparative experiments indicate that the proposed heterogeneous graph convolution model can improve the prediction accuracy.

Accurate traffic flow prediction plays a crucial role in the development of intelligent traffic management. Despite numerous investigations into spatio-temporal methods, achieving high accuracy in traffic flow prediction remains challenging. This challenge arises from the complex dynamic spatio-temporal correlations within the traffic road network and the limitations imposed by the selection of hyperparameters based on experiments and manual experience, which can affect the performance of the network architecture. This paper introduces a novel transformer-based adaptive graph convolutional recurrent network. The proposed network automatically infers the interdependencies among different traffic sequences and incorporates the capability to capture global spatio-temporal correlations. This enables the dynamic capture of long-range temporal correlations. Furthermore, the whale optimization algorithm is employed to efficiently design an optimal network structure that aligns with the requirements of the traffic domain and maximizes the utilization of limited computational resources. This design approach significantly enhances the model’s performance and improves the accuracy of traffic flow prediction. The experimental results on four real datasets demonstrate the efficacy of our approach. In PEMS03, it improves MAE by 2.6% and RMSE by 1.4%. In PEMS04, improvements are 1.6% in MAE and 1.4% in RMSE, with a similar MAPE score to the best baseline. For PEMS07, our approach shows a 4.1% improvement in MAE and 2.2% in RMSE. On PEMS08, it surpasses the current best baseline with a 3.4% improvement in MAE and 1.6% in RMSE. These results confirm the good performance of our model in traffic flow prediction across multiple datasets.

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.

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.

Traffic flow forecasting is the basis for the dynamic control and application of Intelligent Transport Systems (ITS), which is of great practical importance in reducing road congestion. Accurately predicting traffic flow remains a significant obstacle due to the intricate spatial and temporal dependencies associated with traffic movement. To capture the simultaneous dynamics of traffic flow’s spatiotemporal features, this paper suggests a novel Spatiotemporal Interactive Dynamic Synchronous Graph Convolution Network (STIDSG) for traffic flow forecasting, which is composed of the Interactive Dynamic Graph Convolution Network (IDGCN) and the Spatiotemporal Synchronous Graph Convolution Layer (STSGCL). IDGCN learns and shares features using an interactive learning strategy that is based on Dynamic Graph Convolution (DGCN). By incorporating DGCN into the interactive learning structure, it is possible to learn dynamic spatial features of traffic flow in an interactive manner while also capturing temporal dependencies. STSGCL effectively captures the intricate local and dynamic correlations of traffic flows by designing numerous Spatiotemporal Synchronization Graph Convolution Modules (STSGCM) for different time periods. The results of the experiment demonstrate that the STIDSG model suggested in this paper can proficiently extract the changing spatiotemporal features of the traffic flow and outperforms the commonly used baseline methods for prediction. The results of the experiment demonstrate that the STIDSG model suggested in this paper can proficiently extract the changing spatiotemporal features of the traffic flow and outperforms the commonly used baseline methods for prediction.

As a cornerstone of intelligent transportation systems, traffic flow prediction has garnered extensive research attention. However, traffic flow data exhibits pronounced spatio-temporal dynamics, rendering accurate traffic flow prediction a challenging endeavor. Existing methodologies compartmentalize the capture of temporal and spatial dependencies through distinct modules, overlooking latent spatio-temporal heterogeneity within traffic data. Furthermore, they often solely consider spatio-temporal correlations between adjacent historical time steps, neglecting the extraction of temporal dependencies across different time intervals. To address these issues, we propose a novel Multi-scale Fusion Dynamic Graph Neural Network (MFDGNN). MFDGNN employs Temporal Convolutional Networks (TCNs) to generate time series on multiple scales, subsequently extracting spatio-temporal features from these sequences to construct mutually independent fusion graphs. By leveraging fusion graphs to extract features from time series on multiple scales, our model adeptly captures spatio-temporal dependencies of traffic nodes across various time dimensions, thereby achieving enhanced precision in traffic flow prediction. Experimental results conducted on four publicly available datasets demonstrate the superiority of our model compared to other baseline approaches.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

Urban traffic flow prediction plays an important role in traffic flow control and urban safety risk prevention and control. Timely and accurate traffic flow prediction can provide guidance for traffic, relieve urban traffic travel pressure and reduce the frequency of accidents. Due to the randomness and fast changing speed of urban dynamic traffic data flow, most of the existing prediction methods lack the ability to model the dynamic temporal and spatial correlation of traffic data, so they cannot produce satisfactory prediction results. A spatio-temporal convolution network (ST-CNN) is proposed to solve the traffic flow prediction problem. The model consists of two parts: 1) a convolution block used to extract spatial features; 2) a block of time used to characterize time. Data has been fully mined through two modules to output the prediction results of spatio-temporal characteristics, and at the same time, skip connection (direct connection) has been made between the two modules to avoid the problem of gradient explosion. The experimental results on two data sets show that ST-CNN is better than the baseline model.