Real-time Traffic Flow Prediction Research Articles

FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network

Predicting traffic flow is vital for optimizing intelligent transportation systems (ITS) and reducing congestion by forecasting traffic patterns accurately. However, current centralized TFP systems have limitations, e.g., slow training, high communication costs, and privacy concerns. To address these challenges, this study proposes FedAGAT, a system for short-term traffic flow prediction (TFP) based on federated community and adaptive spatial-temporal graph attention networks (AGAT). The FedAGAT system allows for local data processing and sharing only model updates with the server. This enables real-time, scalable, and secure TFP - essential capabilities for efficient ITS. AGAT is employed to capture intricate spatial-temporal dependencies in traffic flow, while federated learning facilitates decentralized learning, enhancing privacy. The FedAGAT prediction process involves four steps: dividing the local subnetwork using spectral community detection, locally training based on global parameters, uploading updated parameters, and creating a global model prediction based on the aggregated parameters. To evaluate the performance of FedAGAT, two real-world traffic datasets were utilized for benchmarking against seven statistical and deep learning models. Results demonstrate that FedAGAT provides relatively higher accuracy for short- and mid-term forecasting horizons. Moreover, FedAGAT predictions closely match real traffic flow values, and the overall performance is comparable to a global model, while requiring less time.

A hybrid deep learning model for urban expressway lane-level mixed traffic flow prediction

Precise real-time traffic flow prediction is crucial for route guidance and traffic fine control. With the development of autonomous driving, the mixed traffic flow state composed of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) provides new insight into traffic flow prediction. In this paper, we innovatively consider the interaction between heterogeneous traffic flow as well as the mutual effect of traffic flow on different lanes and develop a hybrid model based on deep learning for urban expressway lane-level mixed traffic flow prediction, including three modules. First, the feature selection module is applied to screen the features with a high spatio-temporal correlation to the prediction object and construct the input matrix. Then, it is input to the feature attention module to quantify the importance of the input features on the prediction object, thereby assigning attention weights. Finally, the spatio-temporal information fusion module is adopted to capture the global spatio-temporal dynamics of traffic flow at horizontal and vertical spatial scales, as well as learn the complex coupling characteristics of heterogeneous traffic flow, thus obtaining predictions. An urban expressway mixed traffic flow simulation environment is built to collect experimental datasets for prediction accuracy evaluation. The results indicate that the proposed model outperforms the benchmarks in single-step and multi-step mixed traffic flow predictions on each lane. Furthermore, the proposed model shows the best performance and strong robustness under different penetration rates of connected automated vehicles.

Fast autoregressive tensor decomposition for online real-time traffic flow prediction

Online real-time traffic flow prediction typically offers better real-time performance than offline prediction. However, existing studies rarely discussed online real-time traffic flow prediction and the balance between prediction accuracy and computational costs. Training and predicting traffic flow data with complex patterns by artificial intelligence models is usually time-consuming. Some high-accuracy statistical learning methods also reach a performance bottleneck in terms of computational speed. Therefore, a Fast Autoregressive Tensor Decomposition (FATD) algorithm is proposed for online real-time traffic flow prediction. First, the historical tensor data is decomposed by Tucker Decomposition into factor matrices and easy-to-compute core tensors, and these core tensors are modeled by Tensor Seasonal Autoregressive Integrated Moving Average (Tensor SARIMA). Second, a future core tensor is predicted by Tensor SARIMA. By the Inverse Tucker Decomposition, the traffic flow data to be predicted is recovered. The experimental results show that the FATD algorithm can reduce the computational cost while maintaining high accuracy. Compared with baselines, the FATD algorithm reduces MAE, RMSE, and the computational cost by approximately 35.04%, 26.86%, and 99.28% on average, respectively.

Traffic flow state prediction based on space-time correlation of vehicle trajectory using the deep hybrid model

In order to alleviate the increasingly serious traffic congestion problem in China, realize intelligent traffic control, and provide accurate and real-time traffic flow prediction data for traffic flow guidance and traffic travel, this paper designs a GPS-based vehicle trajectory fusion optimization deep model BN-LSTM-CNN which makes full use of the temporal and spatial correlation characteristics of dynamic traffic flow to improve the accuracy of short-term traffic flow prediction. The parameters of the historical GPS dynamic trajectory of the traffic network link are converted into a two-dimensional matrix image of time and space relationship. First, the spatial features are input to the CNN network, and the spatial dependence relationship between the links is mined, then the traffic flow time series modeling is performed with a four-layer ConvLSTM network, and the BN normalization layer is added to normalize the activation value of the previous layer on each batch, so that the model can obtain higher training accuracy and quickly complete the prediction of the traffic flow state in a certain period of time in the future. The experimental results show that the prediction model is fast to optimize, the prediction error is the smallest compared with other methods, and it can meet the real-time requirements of urban traffic control.

Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

The vehicular ad hoc network (VANET) is an emerging network technology that has gained popularity because to its low cost, flexibility, and seamless services. Software defined networking (SDN) technology plays a critical role in network administration in the future generation of VANET with fifth generation (5G) networks. Regardless of the benefits of VANET, energy economy and traffic control are significant architectural challenges. Accurate and real-time traffic flow prediction (TFP) becomes critical for managing traffic effectively in the VANET. SDN controllers are a critical issue in VANET, which has garnered much interest in recent years. With this objective, this study develops the SDNTFP-C technique, a revolutionary SDN controller-based real-time traffic flow forecasting technique for clustered VANETs. The proposed SDNTFP-C technique combines the SDN controller’s scalability, flexibility, and adaptability with deep learning (DL) models. Additionally, a novel arithmetic optimization-based clustering technique (AOCA) is developed to cluster automobiles in a VANET. The TFP procedure is then performed using a hybrid convolutional neural network model with attention-based bidirectional long short-term memory (HCNN-ABLSTM). To optimise the performance of the HCNN-ABLSTM model, the dingo optimization technique was used to tune the hyperparameters (DOA). The experimental results analysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.

Predict Vessel Traffic with Weather Conditions Based on Multimodal Deep Learning

Vessel management calls for real-time traffic flow prediction, which is difficult under complex circumstances (incidents, weather, etc.). In this paper, a multimodal learning method named Prophet-and-GRU (P&G) considering weather conditions is proposed. This model can learn both features of the long-term and interdependence of multiple inputs. There are three parts of our model: first, the Decomposing Layer uses an improved Seasonal and Trend Decomposition Using Loess (STL) based on Prophet to decompose flow data; second, the Processing Layer uses a Sequence2Sequence (S2S) module based on Gated Recurrent Units (GRU) and attention mechanism with a special mask to extract nonlinear correlation features; third, the Joint Predicting Layer produces the final prediction result. The experimental results show that the proposed model predicts traffic with an accuracy of over 90%, which outperforms advanced models. In addition, this model can trace real-time traffic flow when there is a sudden drop.

Optimization Algorithm of Urban Rail Transit Network Route Planning Using Deep Learning Technology.

Under the present background, optimizing the existing urban rail transit network is the focus of urban rail transit construction at present. Based on DL, this paper constructs the optimization algorithm of urban rail transit network route planning. According to the current urban layout and urban planning, build a suitable rail transit network line form; according to the function, the types of urban rail transit stations are divided, and the optimization of urban rail transit network lines is realized. In addition, according to the K short path algorithm, this paper calculates the effective path between any stations of rail transit and, according to the model, allocates the passenger flow to each path. Experimental results show that the accuracy of real-time traffic flow prediction by this algorithm can reach 94.98%, which is about 9% higher than other methods. This algorithm can effectively optimize the route planning of urban rail transit network. This verifies the effectiveness of the route planning optimization algorithm proposed in this paper. Using the algorithm in this paper for line planning can get good real time, rationality, and optimality.

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.

Traffic Message Channel Prediction Based on Graph Convolutional Network

With the development of big data, large-scale traffic flow forecasting which is a part of smart transportation has become an increasingly important research direction. Accurate and real-time traffic flow prediction is the key and difficult part of the traffic. The complex spatial topological structure and dynamic traffic flow information in urban roads constitute a changeable spatial correlation, and the daily traffic flow cycle and weekly traffic flow cycle constitute a complex time correlation. For the current mainstream model, there are two main limitations: 1. Most of the existing models only focus on time correlation and ignore spatial correlation. 2. Even if the spatial correlation is concerned, the topological relationship between spaces is not fully considered. This paper proposes a new traffic-flow prediction model, which named Principal Spatio-Temporal Graph Convolution Network (PST-GCN) model, which uses a combination of Principal Component Analysis (PCA), Graph Convolution Network (GCN), and Long Short-Term Memory model (LSTM). Specifically, PCA is used to reduce the dimension of data, GCN is used to learn the network topology of urban roads, LSTM is used to capture the time correlation of traffic flow. By comparing the results of different models, the proposed model is better than the current mainstream models.

Traffic flow prediction over muti-sensor data correlation with graph convolution network

Accurate and real-time traffic flow prediction plays an important role in improving the traffic planning capability of intelligent traffic systems. However, traffic flow prediction is a very challenging problem because the spatial–temporal correlation among roads is complex and changeable. Most of the existing methods do not reasonably analyze the dynamic spatial–temporal correlation caused by the changing relationship of traffic patterns among roads, thus cannot get satisfactory results in the medium and long-term traffic prediction. To address these issues, a novel Multisensor Data Correlation Graph Convolution Network model, named MDCGCN, is proposed in this paper. The MDCGCN model consists of three parts: recent, daily period and weekly period components, and each of which consists of two parts: 1) benchmark adaptive mechanism and 2) multisensor data correlation convolution block. The first part can eliminate the differences among the periodic data and effectively improve the quality of data input. The second part can effectively capture the dynamic temporal and spatial correlation caused by the changing relationship of traffic patterns among roads. Through substantial experiments conducted on two real data sets, results indicate that the proposed MDCGCN model can significantly improve the medium and long-term prediction accuracy for traffic networks of different sizes, and is superior to existing prediction methods.

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

Machine Learning-Based Models for Real-time Traffic Flow Prediction in Vehicular Networks

In recent years, ML-based models are gaining enormous attention from both the automotive industry and academia to support IoVs. Through the accurate prediction of traffic/road conditions, various safety and infotainment applications can efficiently utilize the network entities and enhance the quality of service. Topology control and mobility management protocols in IoVs, among others, would achieve higher efficiency through the support of real-time traffic flow forecasting. However, the current research trend on improving prediction accuracy refrains from answering the essential question of whether ML-based prediction schemes are suitable for real-time traffic prediction. To answer this question, a thorough extensive study to evaluate the efficiency of prediction- based traffic flow schemes is required. In this article, we investigate the effectiveness of various ML-based prediction models by considering both the prediction accuracy and computational time cost. Accordingly, we present rigorous quantitative analysis to identify the important factors that may restrict the use of ML-based prediction models to support real-time services in the IoV environment.

Research on GSTAR-SVM Traffic Prediction Model Based on Wavelet Transform

With the continuous advancement of urbanization in China, the number of urban vehicles has increased rapidly, and traffic congestion has become an urgent problem for modern cities. Therefore, accurate real-time traffic flow prediction is of great significance for solving this problem. However, traffic flow, as a special type of prediction object, has the characteristics of complexity, uncertainty and nonlinearity, which bring great difficulties to the prediction, and the external spatial correlation also has a great influence on the prediction results. In this paper, based on the above problems, the GSTAR-SVM traffic prediction model based on wavelet transform is constructed to predict the short-term traffic flow, and the model is verified based on the data. The experimental results show that the proposed model has higher prediction accuracy.

SDN-based real-time urban traffic analysis in VANET environment

Accurate and real-time traffic flow prediction plays a central role for efficient traffic management. Software Defined Networking (SDN) is one of the key concerns in networking that has caught much attention in recent years. Extensive advancement in software-based configurable equipment has made ready for a new networking paradigm called software-defined vehicular networks (SDVNs). In this paper, we propose a data-driven approach for implementing an artificially intelligent model for vehicular traffic behavior prediction. We combine the flexibility, scalability, and adaptability leveraged by the SDVN architecture along with the machine learning algorithms to model the traffic flow efficiently. First, we introduce an ingenious approach to find congestion sensitive spots in the VANET by means of clustering algorithm and then predicting the future traffic densities for each spot by recurrent neural networks (RNNs). Neural networks (NNs) have been extensively used to model short-term traffic prediction in the past years. We construct a long short-term memory neural network (LSTM-NN) architecture which overcomes the issue of back-propagated error decay through memory blocks for spatiotemporal traffic prediction with high temporal dependency. Due to such learning ability, LSTM can capture the stochastic characteristics and non-linear nature of the traffic flow. The performance evaluation of the proposed approach shows that it has the potential to predict real-time traffic trends accurately with Mean Squared Error (MSE) of 3.0e−3 in the scaled metrics, which is equivalent to 97% accuracy in traffic density prediction.

A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control

Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.

MRF model based real‐time traffic flow prediction with support vector regression

A novel statistical model for real-time traffic flow prediction is proposed. In this study, a 3D Markov random field (MRF) is used to model the temporal dynamics of the traffic flow measured by VDS sensor network. Then, the spatial and temporal relations between roads at a given location are represented by the 3D graph using cliques, then its structure is determined by clique parameters. Here, a support vector regression is adopted for estimating the correlation parameters. The technique is applied to actual traffic flow data from Gyeongbu expressway, South Korea. The experiments demonstrate that the proposed method can predict the traffic flow with an accuracy of 85.6%, which improves 17.3% of the existing state-of-the-art method.

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research.

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction.

Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzyc-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

Short-term Traffic Flow Prediction Based on Multivariable Phase Space Reconstruction and LSSVM

Real-time and accurate short-term traffic flow prediction is the premise and key of intelligent traffic control and guidance system. According to this problem, this paper put forward a prediction model based on multivariable phase space reconstruction and least squares support vector machine (LSSVM). First, the model confirms embedding dimension and delay time of the traffic flow, occupancy and average speed time series by analyzing their chaotic characteristics, and reconstructs multivariable state space. Second, the phase points obtained after reconstruction are as input, and the last traffic flow parameters came from the following phase points are as output. Finally, the LSSVM which be trained is adapted to realize short-term traffic flow prediction. This research compares this model with a model based on univariate phase space reconstruction and LSSVM, and the results show that the model proposed in this paper predicts better.