Accurate Traffic Prediction Research Articles

IGCRRN: Improved Graph Convolution Res-Recurrent Network for spatio-temporal dependence capturing and traffic flow prediction

Accurate traffic flow prediction is critical for traffic management and route guidance, enabling urban traffic to be free-flowing conditions and maximizing transport efficiency. In current prediction methods, the simple and fixed spatial graph only uses the prior knowledge of the traffic network, resulting in weak prediction performance. This paper proposes an Improved Graph Convolution Res-Recurrent Network (IGCRRN), which relies on uncertain spatio-temporal information for traffic flow prediction. In particular, a spatial dependence matrix that combines the origin graph matrix and the data-generated embedding node matrix is created. In this way, the spatial connection relationship can be obtained from the static graph information and changing traffic flow series, making the improved graph convolution block infer and quantify the different contributions in both spatial dependence and temporal dependence in a data-driven manner. In addition, the residual structure is employed to model the multi-level spatial dependence, and the IGCRRN-cell units based on the residual connection block and LSTM are designed to make the model automatically capture the spatio-temporal dependence in the traffic flow sequence. Experiments are conducted on two real traffic datasets, and the experiment results show that our proposed spatial dependence matrix can investigate the valuable information and consider the heterogeneity in the traffic flow. The IGCRRN model outperforms the baseline and state-of-the-art methods in prediction performance.

LST-GCN: Long Short-Term Memory Embedded Graph Convolution Network for Traffic Flow Forecasting

Traffic flow prediction is an important part of the intelligent transportation system. Accurate traffic flow prediction is of great significance for strengthening urban management and facilitating people’s travel. In this paper, we propose a model named LST-GCN to improve the accuracy of current traffic flow predictions. We simulate the spatiotemporal correlations present in traffic flow prediction by optimizing GCN (graph convolutional network) parameters using an LSTM (long short-term memory) network. Specifically, we capture spatial correlations by learning topology through GCN networks and temporal correlations by embedding LSTM networks into the training process of GCN networks. This method improves the traditional method of combining the recurrent neural network and graph neural network in the original spatiotemporal traffic flow prediction, so it can better capture the spatiotemporal features existing in the traffic flow. Extensive experiments conducted on the PEMS dataset illustrate the effectiveness and outperformance of our method compared with other state-of-the-art methods.

TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality

Accurate traffic congestion prediction is crucial for efficient urban intelligent transportation systems (ITS). Though most existing methods attempt to characterize spatial correlation and temporal correlation in traffic congestion, few of them consider spatial heterogeneity and temporal heterogeneity: spatial correlation depends on temporality, and temporal correlation depends on spatiality in traffic congestion. To address this problem, this paper proposes a novel approach called TCP-BAST with bilateral alternation to simultaneously capture both the correlation and the heterogeneity between spatiality and temporality to improve traffic congestion prediction. First, to capture spatial correlation and spatial heterogeneity, we propose a spatial–temporal alternation (STA) module with multi-head graph attention networks and temporal embedding. Second, to capture temporal correlation and temporal heterogeneity, we propose a temporal-spatial alternation (TSA) module with multi-head masked attention networks and spatial embedding. Third, to predict the traffic congestion of multiple road sections in a traffic network, we propose a spatial–temporal fusion (STF) module to fuse the multi-grained spatial-temporal features derived from the STA and TSA modules. The experimental results on a real-world traffic dataset demonstrate that the proposed TCP-BAST approach outperforms the baseline methods in terms of both the mean absolute error (MAE) and the root mean squared error (RMSE). Both spatial-temporal alternation and temporal-spatial alternation are important for improving traffic congestion prediction, with the former being more critical than the latter.

Spatiotemporal Attention-Based Graph Convolution Network for Segment-Level Traffic Prediction

Traffic prediction, as a core component of intelligent transportation systems (ITS), has been investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still remains an open challenge due to the nonlinearities and complex patterns of traffic flows. In addition, most of the existing traffic prediction methods focus on grid-based computing problems (e.g., crowd in-out flow prediction) and point-based computing problems (e.g., traffic detector data prediction), ignoring the segment-based traffic prediction tasks. In this study, we propose an attention-based spatiotemporal graph attention network (AST-GAT) for segment-level traffic speed prediction. In particular, a multi-head graph attention block is designed to capture the spatial dependencies among road segments. Then, a component fusion block is built for speed, volume, and weather information integration. Finally, an attention-based Long short-term memory (LSTM) block is constructed for temporal dependency learning as well as segment-based speed prediction. Experiments on a real-world dataset from Highways England demonstrate that the proposed AST-GAT model outperforms the state-of-the-art baselines, providing an efficient tool for segment-based traffic prediction and therefore filling the gap between point-based and grid-based predictions.

A Hybrid Framework Model Based on Wavelet Neural Network with Improved Fruit Fly Optimization Algorithm for Traffic Flow Prediction

Accurate traffic flow prediction can provide sufficient information for the formation of symmetric traffic flow. To overcome the problem that the basic fruit fly optimization algorithm (FOA) is easy to fall into local optimum and the search method is single, an improved fruit fly optimization algorithm (IFOA) based on parallel search strategy and group cooperation strategy is proposed. The multi-swarm mechanism is introduced in the parallel search strategy, in which each subswarm is independent and multiple center positions are determined in the iterative process, thereby avoiding the problems of reduced diversity and premature convergence. To increase communication between fruit fly subswarms, the informative fruit flies selected from subswarms are guided by the randomly generated binary fruit fly to achieve the crossover operation in the group cooperation strategy. Then a hybrid framework model based on wavelet neural network (WNN) with IFOA (IFOA-WNN) for traffic flow prediction is designed, in which IFOA is applied to explore appropriate structure parameters for WNN to achieve better prediction performance. The simulation results verify that the IFOA can provide high-quality structural parameters for WNN, and the hybrid IFOA-WNN prediction model can achieve higher prediction accuracy and stability than the compared methods.

WT-2DCNN: A convolutional neural network traffic flow prediction model based on wavelet reconstruction

Accurate traffic flow prediction is important for congestion identification and traffic dispersion. The original traffic flow data may generate different noises in the detector collection process and data aggregation process, resulting in large errors in the prediction results.To solve the problems above, this paper proposes a convolutional neural network model based on wavelet reconstruction (WT-2DCNN), which eliminates potential outliers in the data by introducing wavelet method, and constructs a WT-2DCNN model based on data extension and fusion of convolutional neural networks, and obtains the internal trend features of traffic flow through multiple convolutional and pooling layers in this model for traffic flow prediction.In this paper, the performance and training efficiency of the WT-2DCNN model has been validated on the publicly available Caltrans Performance Measurement System (PeMS) dataset, and the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are significantly lower than those of other algorithms, and the training time is only 1/4 to 1/3 of that of Recurrent Neural Networks (RNN), indicating that the WT-2DCNN model has higher accuracy and more efficient training efficiency.

ST-MGAT:Spatio-temporal multi-head graph attention network for Traffic prediction

Traffic flow prediction is an important part of the intelligent transportation system, and accurate traffic flow prediction can better perform the data management and control of the urban road network. Traditional methods often ignore the interaction between traffic flow factors and the spatiotemporal dependence of traffic networks. In this paper, a spatiotemporal multi-head graph attention network (ST-MGAT) is proposed for the traffic flow prediction task. In the input layer, multiple traffic flow variables are used as input to learn the nonlinearity and complexity existing in it. In terms of modeling, the structure of linear gating units is transformed using the full volume and applied to correlation learning in the temporal dimension. The spatial dimension features are then captured using a multi-head attention map network. Ablation experiments are designed to verify the use effect of each module. At the same time, several sets of parameter experiments are designed to obtain the best model parameters. The experimental results show that compared with the baselines, the RMSE of the ST-MGAT model is decreased by almost 2.340%∼22.471%, and the MAE is decreased by almost 7.417%∼26.142%. It is proved that the ST-MGAT model has high performance on the traffic flow prediction task.

Spatial-Temporal Chebyshev Graph Neural Network for Traffic Flow Prediction in IoT-Based ITS

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control, and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. The traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues, including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we first add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other eight baseline methods on real-world traffic data sets PeMS.

Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been devoted to this area, especially deep learning method, greatly advancing traffic prediction abilities. The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives. Specifically, we first summarize the existing traffic prediction methods, and give a taxonomy. Second, we list the state-of-the-art approaches in different traffic prediction applications. Third, we comprehensively collect and organize widely used public datasets in the existing literature to facilitate other researchers. Furthermore, we give an evaluation and analysis by conducting extensive experiments to compare the performance of different methods on a real-world public dataset. Finally, we discuss open challenges in this field.

A Bidirectional Context-Aware and Multi-Scale Fusion Hybrid Network for Short-Term Traffic Flow Prediction

Short-term traffic flow prediction is to automatically predict the traffic flow changes in a period of future time based on the extraction of the spatiotemporal features in the road network. For governments, timely and accurate traffic flow prediction is crucial to plan road manage-ment and improve traffic efficiency. Recent advances in deep learning have shown their dominance on short-term traffic flow prediction. However, previous methods based on deep learning are mainly limited to temporal features and have so far failed to predict the bidirectional con-textual spatiotemporal relationship correctly. Besides, the precision and the practicality are limited by the road network scale and the single time scale. To remedy these issues, a Bidirectional Context-aware and Multi-scale fusion hybrid Network (BCM-Net) is proposed, which is a novel short-term traffic flow prediction framework to predict timely and accurate traffic flow changes. In BCM-Net, the Bidirectional Context-aware (BCM) block is added to the feature extraction structure to effective-ly integrate spatiotemporal features. The Interpolation Back Propagation sub-network is used to merge multi-scale information, which further improves the robustness of the model. Experiment results on diverse datasets demonstrated that the proposed method outperformed the state-of-the-art methods.

A grey convolutional neural network model for traffic flow prediction under traffic accidents

Accurate traffic flow prediction can effectively improve traffic efficiency and safety. This has become a trending topic in intelligent transportation systems. However, the occurrence of traffic accidents will cause great fluctuations of traffic flow in a short time, thus affecting the accuracy of flow prediction. This paper analyses the influence of traffic accident information on traffic flow, and proposes a grey convolutional neural network called G-CNN for traffic flow prediction. First, considering the small sample size of traffic accidents and the incomplete information description, the traffic flow and traffic speed change rate are defined to represent traffic accident grey information, and a grey fixed-weight clustering method for extracting the characteristics of traffic accident grey information is established. Second, the spatiotemporal characteristics of traffic flow are analysed, and a third-order tensor is developed to fuse them with the accident characteristics and serve as the input of the G-CNN. Then the G-CNN model is built to predict traffic flow in a traffic accident environment. The Canadian Whitemud Drive experiment on traffic flow without traffic accident information reveals that the developed G-CNN model has a better ability to predict future traffic flow with better accuracy and stability. Meanwhile, it is verified that the fluctuation of the traffic change rate will cause traffic accidents, and the probability of traffic accidents at a certain time point will be obtained through feature extraction of traffic accidents. Finally, the model is verified for the traffic flow of Hangzhou Viaduct in China, which demonstrates that the G-CNN model outperforms all of the compared models.

TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network

Traffic forecasting constitutes a task of great importance in intelligent transport systems. Owing to the non-Euclidean structure of traffic data, the complicated spatial correlations, and the dynamic temporal dependencies, it is challenging to predict traffic accurately. Despite the fact that few prior studies have considered the interconnections between multiple traffic nodes at the same timestep, the majority of studies fail to capture the dependencies among multiple nodes at different timesteps. Furthermore, most existing work generates shallow graphs based solely on the distance between traffic nodes, which limits their representation competence and declines their power in capturing complex correlations. In particular, inspired by the recent breakthroughs in the generative adversarial network (GAN) and the power of the graph convolution network (GCN) in handling non-Euclidean data, this paper puts forward an adversarial multi-graph convolutional neural network model, named TFGAN, to address the abovementioned problems. We integrate the unsupervised model elasticity with the supervision provided by supervised training to help the GAN generator model generates accurate traffic predictions. To improve the representation and model the implicit correlations effectively, multiple GCNs are constructed within the generator based on various perspectives, such as similarity, correlation, and spatial distance. Meanwhile, GRU and self-attention are applied after each graph to capture the dynamic temporal dependencies across nodes. The comprehensive experiments on three different traffic variables (traffic flow, speed, and travel time) using six real-world traffic datasets demonstrate that TFGAN outperforms the related state-of-the-art models and achieves significant results.

An Attention Encoder-Decoder Dual Graph Convolutional Network with Time Series Correlation for Multi-Step Traffic Flow Prediction

Accurate traffic prediction is a powerful factor of intelligent transportation systems to make assisted decisions. However, existing methods are deficient in modeling long series spatio-temporal characteristics. Due to the complex and nonlinear nature of traffic flow time series, traditional methods of prediction tasks tend to ignore the heterogeneity and long series dependencies of spatio-temporal data. In this paper, we propose an attentional encoder-decoder dual graph convolution model with time-series correlation (AED-DGCN-TSC) for solving the spatio-temporal sequence prediction problem in the traffic domain. First, the time-series correlation module calculates the sequence similarity by fast Fourier transform and inverse fast Fourier transform, while obtaining multiple possible lengths as possible solutions for the sequence period length. Then, K possible periods fetches are selected and the corresponding sequences are weighted and aggregated to the target sequence. Then, the gated dual graph convolution recurrent unit uses the graph convolution operation, which combines the ideas of node embedding, and dual graph, as an operation inside the gated recurrent structure to capture the spatio-temporal heterogeneity relationship of long sequences. The gated decomposition recurrent module decomposes the time series into the period and trend terms, which are modelled by convolutional gated recurrent unit (ConvGRU) and then fused with features, respectively, and output after graph convolution. Finally, multi-step prediction of future traffic flow is performed in the form of encoder-decoder. Experimental evaluations are conducted on two real traffic datasets, and the results demonstrate the effectiveness of the proposed model.

Traffic Flow Prediction: An Intelligent Scheme for Forecasting Traffic Flow Using Air Pollution Data in Smart Cities with Bagging Ensemble

Traffic flow prediction is the most critical part of any traffic management system in a smart city. It can help a driver to pick the most optimized way to their target destination. Air pollution data are often connected with traffic congestion and there exists plenty of research on the connection between air pollution and traffic congestion using different machine learning approaches. A scheme for efficiently predicting traffic flow using ensemble techniques such as bagging and air pollution has not yet been introduced. Therefore, there is a need for a more accurate traffic flow prediction system for the smart cities. The aim of this research is to forecast traffic flow using pollution data. The contribution is twofold: Firstly, a comparison has been made using different simple regression techniques to find out the best-performing model. Secondly, bagging and stacking ensemble techniques have been used to find out the most accurate model of the two comparisons. The results show that the K-Nearest Neighbors (KNN) bagging ensemble provides far better results than all the other regression models used in this study. The experimental results show that the KNN bagging ensemble model reduces the error rate in predicting the traffic congestion by more than 30%.

Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network.

Network traffic prediction is an important tool for the management and control of IoT, and timely and accurate traffic prediction models play a crucial role in improving the IoT service quality. The degree of burstiness in intelligent network traffic is high, which creates problems for prediction. To address the problem faced by traditional statistical models, which cannot effectively extract traffic features when dealing with inadequate sample data, in addition to the poor interpretability of deep models, this paper proposes a prediction model (fusion prior knowledge network) that incorporates prior knowledge into the neural network training process. The model takes the self-similarity of network traffic as a priori knowledge, incorporates it into the gating mechanism of the long short-term memory neural network, and combines a one-dimensional convolutional neural network with an attention mechanism to extract the temporal features of the traffic sequence. The experiments show that the model can better recover the characteristics of the original data. Compared with the traditional prediction model, the proposed model can better describe the trend of network traffic. In addition, the model produces an interpretable prediction result with an absolute correction factor of 76.4%, which is at least 10% better than the traditional statistical model.

Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction

Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemporal networks have been proposed for traffic prediction, most of them rely on predefined graphs from different views or static adaptive matrices. Some implicit dynamics of inter-node dependency may be neglected, which limits the performance of prediction. To address this problem and make more accurate predictions, we propose a traffic prediction model named Time-Evolving Graph Convolution Recurrent Network (TEGCRN), which takes advantage of time-evolving graph convolution to capture the dynamic inter-node dependency adaptively at different time slots. Specifically, we first propose a tensor-composing method to generate adaptive time-evolving adjacency graphs. Based on these time-evolving graphs and a predefined distance-based graph, a graph convolution module with mix-hop operation is applied to extract comprehensive inter-node information. Then the resulting graph convolution module is integrated into the Recurrent Neural Network structure to form an general predicting model. Experiments on two real-world traffic datasets demonstrate the superiority of TEGCRN over multiple competitive baseline models, especially in short-term prediction, which also verifies the effectiveness of time-evolving graph convolution in capturing more comprehensive inter-node dependency.

DGSLSTM: Deep Gated Stacked Long Short-Term Memory Neural Network for Traffic Flow Forecasting of Transportation Networks on Big Data Environment.

Deep learning and big data techniques have become increasingly popular in traffic flow forecasting. Deep neural networks have also been applied to traffic flow forecasting. Furthermore, it is difficult to determine whether neural networks can be used for accurate traffic flow prediction. Moreover, since the network model is poorly structured and the parameter optimization technique is inappropriate, the traffic flow prediction is inaccurate because of the lack of certainty. The proposed system overcomes these problems by combining multiple simple recurrent long short-term memory (LSTM) neural networks with time traits to predict traffic flow using a deep gated stacked neural network. To deepen the model, the hidden layers have been trained using an unsupervised layer-by-layer approach. This approach provides a systematic representation of the time series data. A systematic representation of hidden layers improves the accuracy of time series forecasting by capturing information at multiple levels. Furthermore, it emphasizes the importance of model structure, random weight initialization, and hyperparameters used in stacked LSTM to enhance predictive performance. The prediction efficacy of the deep gated stacked LSTM model is compared with that of the gated recurrent unit model and the stacked autoencoder model.

Hybrid Deep Learning Approach for Traffic Speed Prediction.

Traffic speed prediction plays a fundamental role in traffic management and driving route planning. However, timely accurate traffic speed prediction is challenging as it is affected by complex spatial and temporal correlations. Most existing works cannot simultaneously model spatial and temporal correlations in traffic data, resulting in unsatisfactory prediction performance. In this article, we propose a novel hybrid deep learning approach, named HDL4TSP, to predict traffic speed in each region of a city, which consists of an input layer, a spatial layer, a temporal layer, a fusion layer, and an output layer. Specifically, first, the spatial layer employs graph convolutional networks to capture spatial near dependencies and spatial distant dependencies in the spatial dimension. Second, the temporal layer employs convolutional long short-term memory (ConvLSTM) networks to model closeness, daily periodicity, and weekly periodicity in the temporal dimension. Third, the fusion layer designs a fusion component to merge the outputs of ConvLSTM networks. Finally, we conduct extensive experiments and experimental results to show that HDL4TSP outperforms four baselines on two real-world data sets.

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

Accurate traffic prediction can effectively alleviate traffic congestion problems. The complex spatial correlation of traffic flow contributes to the challenging prediction problem. Most current prediction methods focus on learning local spatial correlation, ignoring the spatial correlation of long-distance traffic flow. In this paper, we combine the improved Graph Convolutional Network (GCN) with Gated Recurrent Unit (GRU) to propose a hybrid model integrating local and global spatial correlation (T-LGGCN) for traffic prediction. The model consists of two parts: global spatial-temporal component and local spatial-temporal component. For the global spatial-temporal component, we construct the global correlation matrix to improve the GCN for obtaining the global spatial correlation. And GRU is stacked to obtain the global spatial-temporal correlation. For the local spatial-temporal component, we utilize the strategy of combining Fully Connected Layer (FCL) and GCN to analyze the local spatial correlation. Similarly, GRU is used to perform the output of local spatial-temporal correlation. The output of the two components is finally summed, and the prediction results are generated with the dense network. Experiments were conducted using the highway datasets PEMS04 and PEMS08 from the Caltrans Performance Measurement System, and the results show that our model significantly outperforms state-of-the-art baselines.

Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

Due to the advances of intelligent transportation system (ITSs), traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control, navigation, route mapping, etc. The traffic prediction model aims to predict the traffic conditions based on the past traffic data. For more accurate traffic prediction, this study proposes an optimal deep learning-enabled statistical analysis model. This study offers the design of optimal convolutional neural network with attention long short term memory (OCNN-ALSTM) model for traffic prediction. The proposed OCNN-ALSTM technique primarily pre-processes the traffic data by the use of min-max normalization technique. Besides, OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases. For enhancing the predictive outcomes of the OCNN-ALSTM technique, the bird swarm algorithm (BSA) is employed to it and thereby overall efficacy of the network gets improved. The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work. The experimental validation of the OCNN-ALSTM technique is performed using benchmark datasets and the results are examined under several aspects. The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.