Short-term Traffic Flow Research Articles

Frequent Congestion Detection Model Based on Critical Intersection Identification

Intersection information in urban transportation networks is critical to the safe and efficient operation of road traffic. The controllability of the road network can be increased by controlling critical intersections during peak traffic periods. On the one hand, based on the improved intra- and inter-level correlation description matrix of time steps, a directed supra-adjacency matrix (DSAM) temporal network model is built to verify the validity of the model using the road network data of urban core areas and compare the results with those obtained from static critical intersection identification. The results show that the DSAM model has a phase continuity feature in ranking the intersection importance, which has richer connotation than the critical intersection identification results based on a single indicator and has better consistency at different time granularity. On the other hand, based on the DSAM model to identify critical intersections of road network, short-term traffic flow prediction is performed with the help of a road network adjacency matrix, and the results show that the Chebyshev diagram convolution neural network (ChebNet) has the best performance. It can be used in the detection of recurrent congestion of urban road networks, and corresponding control measures are made in shorter time steps, which can prevent the possibility of widespread congestion and reduce the blockage of the whole road network. In summary, the model can be used for critical intersection identification and congestion detection in urban road networks.

Channel attention-based spatial-temporal graph neural networks for traffic prediction

PurposeTraffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.Design/methodology/approachThis paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.FindingsThe proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.Originality/value(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

Improved Equilibrium Optimizer for Short-Term Traffic Flow Prediction

Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities of exploration and exploitation. It utilizes the grey wolf optimizer (GWO) to further search the optimal solutions acquired by equilibrium optimizer (EO) and does not add extra evaluation of objective function. The short-term prediction of traffic flow has the characteristics of high non-linearity and uncertainty and has a strong correlation with time. This paper adopts the structure of LSTM and EO-GWO to implement the prediction, and the hyper parameters of the LSTM are optimized by EO-GWO to transcend the problems of backpropagation. Experiments show that the algorithm has achieved wonderful results in the accuracy and computation time of the three prediction models in the highway intersection.

Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting

Traffic flow prediction is the basis of dynamic strategies and applications of intelligent transportation systems (ITS). Accurate traffic flow prediction is of great practical significance in alleviating road congestion and reducing urban road traffic safety hazards. It is challenging since the traffic flow has highly non-linear and complex patterns due to external factors such as time and space. Due to the high stochasticity and uncertainty of traffic flow, the difficulty of traffic flow prediction increases gradually with increasing time steps. The prediction performance of most existing short-term traffic flow prediction methods deteriorates rapidly for longer time steps. In addition, different methods are compared on the same time-granularity dataset, leaving the adaptability and robustness of these methods undervalidated. To address the above challenges, a new traffic forecasting method, named Attention-Based Gated Recurrent Graph Convolutional Network (AGRGCN) is presented for short-term traffic flow prediction. The method can extract spatialtemporal dependencies in traffic flow. In addition, an attention mechanism, which can adaptively capture traffic data relationships at different time steps, is introduced to alleviate the problem of faster deterioration of model prediction performance for longer time steps. Using a road network distance-based graph enables the method better to capture the topological information in traffic flow data. Experiments were conducted on two traffic datasets with different time granularity to predict traffic flow in highway and urban contexts. The experimental results show that our model has certain advantages.

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.

Spatial-Temporal Attention Graph Convolution Network on Edge Cloud for Traffic Flow Prediction

Accurate short-term traffic flow prediction plays an important role in providing road condition information in the immediate future. With the information, intelligent vehicles can plan and adjust the route to prevent congestion. As a result, many models for short-term traffic flow forecasting have been proposed to date. However, most of them focus on the prediction of the entire traffic network, which could lead to several problems: (1) the entire traffic network could have a large scale and a complex structure, for which the model training is likely to be time-consuming as well as inefficient; (2) processing a large amount of training data on the central cloud could cause much calculation pressure on the server and increase the risk of privacy leakage. In this paper, we propose a Spatial-Temporal Attention Graph Convolution Network on Edge Cloud model (STAGCN-EC). We first divide the entire traffic network into several parts to reduce its scale and complexity. Then, we allocate each part of the network to a certain Roadside Unit (RSU) for training, thus there is no need to process all data on the central server. Besides, we utilize spatial-temporal attention and features extracting module that fits the low computational power devices like RSUs, to capture spatial-temporal dependence and predict traffic flow. At last, we use two highway datasets from District 7 and District 4 in California to validate our model. Through the experiments, we find out that our model performs well both in predicted precision and efficiency compared with the five baseline methods.

Short-term traffic flow prediction model based on a shared weight gate recurrent unit neural network

Accurate traffic flow prediction is critical for enhancing traffic network operational efficiency. With the continuous expansion of traffic networks, providing reliable and efficient multi-step traffic flow prediction for large-scale traffic networks with a large number of sensors deployed has become a challenging issue. In this paper, we propose a multi-step many-to-many traffic prediction model for large-scale traffic networks, called spatio-temporal Shared GRU (STSGRU), which receive inputs from multiple sensors and provides predictions for all sensors simultaneously. First, we model the weekly pattern of traffic flow, using periodicity to explore long-term temporal features and provide smooth traffic flow to reduce the impact of data volatility. Second, different from existing models, we propose a shared weight mechanism to achieve many-to-many prediction without mapping traffic networks to images or graph structures. The proposed model strikes a delicate balance between complexity and accuracy. We validate the effectiveness of the proposed method on the Caltrans Performance Measurement System (PeMS) dataset. The results show that our model achieves similar prediction performance with advanced graph neural networks and has higher flexibility.

Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model

The increase of road traffic in large cities during the last years has produced that long and short-term traffic flow forecasting is a critical need for the authorities. The availability of good traffic flow prediction methods is a must to make informed decisions concerning (punctual) traffic congestions. Previous work has shown that the accuracy of these methods decreases if we consider urban traffic and long-term predictions. In this paper we present a hybrid model, combining a Convolutional Neural Network and a Bidirectional Long–Short-Term Memory network, and apply it to long-term traffic flow prediction in urban routes. This model combines the capability of CNN to extract hidden valuable features from the input model and the capability of BiLSTM to understand the temporal context. In order to assess the usefulness of our model, we considered four streets of the city of Madrid with different characteristics and compared the results of our proposed model with the ones obtained by eight widely used baseline models. The results show that our hybrid model outperforms the baseline models with respect to three metrics commonly used in regression: mean absolute error, root mean squared error and accuracy.

Short-Term Traffic Flow Prediction Based on a K-Nearest Neighbor and Bidirectional Long Short-Term Memory Model

In the previous research on traffic flow prediction models, most of the models mainly studied the time series of traffic flow, and the spatial correlation of traffic flow was not fully considered. To solve this problem, this paper proposes a method to predict the spatio-temporal characteristics of short-term traffic flow by combining the k-nearest neighbor algorithm and bidirectional long short-term memory network model. By selecting the real-time traffic flow data observed on high-speed roads in the United Kingdom, the K-nearest neighbor algorithm is used to spatially screen the station data to determine the points with high correlation and then input the BILSTM model for prediction. The experimental results show that compared with SVR, LSTM, GRU, KNN-LSTM, and CNN-LSTM models, the model proposed in this paper has better prediction accuracy, and its performance has been improved by 77%, 19%, 18%, 22%, and 13%, respectively. The proposed K-nearest neighbor-bidirectional long short-time memory model shows better prediction performance.

Establishment and simulation of RMEA-WNN-LSTM model for short-term traffic flow prediction

The research heat of artificial intelligence is increasing, and intelligent transportation is a direction of artificial intelligence. Short-term traffic flow prediction is the embodiment of use of artificial intelligence. In view of the problem that there is no communication between subgroups and the diversity of groups is limited after the convergence operation of mind evolutionary algorithm, this paper introduces learning mechanism and reflection mechanism to improve the mind evolutionary algorithm (RMEA). Through learning mechanism, each subgroup can obtain the winning individual information of all other subgroups on the premise of maintaining its own characteristics, and generating new individuals. After the learning mechanism, the reflection mechanism is used to select the best individuals, and the RMEA-WNN prediction model is constructed. Moreover, taking the prediction residual of model as the data set, the LSTM model is used to forecast the data of traffic flow residual error, and the RMEA-WNN-LSTM prediction model is constructed. The simulation prediction accuracy of the complex model reaches 96.8%, which proves that the model has practical application value.

Formula omitted]-LSTM: An error distribution free deep learning for short-term traffic flow forecasting

Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term memory (LSTM) network is considered qualified to capture the long-term temporal dependencies in traffic flow. However, the training of LSTM networks is often guided by the mean square error (MSE) criterion. Such criterion depends on a strong assumption that the errors between the traffic flow and its predictions are Gaussian independent identically distributed. In this regard, the forecasting performance is seriously deteriorated by non-Gaussian noises inside the traffic flow sequences. To address this issue, we relax the assumption of the prediction errors to arbitrary distribution by a negative guided mixed correntropy criterion. Then, we formulate a robust loss function by the negative guided mixed correntropy criterion. We subsequently equip the loss function in an LSTM network, termed Δfree-LSTM, for short-term traffic flow forecasting. Extensive experiments on four benchmark datasets demonstrate that the Δfree-LSTM network outperforms the traditional parametric and nonparametric models, as well as state-of-the-art LSTM family models. The source code is available athttps://github.com/541764418/Delta-free-LSTM.

Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism

Traffic-flow prediction plays an important role in the construction of intelligent transportation systems (ITS). So, in order to improve the accuracy of short-term traffic flow prediction, a prediction model (GWO-attention-LSTM) based on the combination of optimized attention mechanism and long short-term memory (LSTM) is proposed. The model is based on LSTM and uses the attention mechanism to assign individual weight to the feature information extracted via LSTM. This can increase the prediction model’s focus on important information. The initial weight parameters of the attention mechanism are also optimized using the grey wolf optimizer (GWO). By simulating the hunting process of grey wolves, the GWO algorithm calculates the hunting position of the grey wolf and maps it to the initial weight parameters of the attention mechanism. In this way, the short-time traffic flow prediction model is constructed. The traffic flow data of the trunk roads in the center of Qingdao (China) are used as the research object. Multiple sets of comparison models are set up for prediction analysis. The results show that the GWO-attention-LSTM model has obvious advantages over other models. The prediction error MAE values of the GWO-attention-LSTM model decreased by 7.32% and 14.35% on average compared with the attention-LSTM model and LSTM model. It is concluded that the GWO-attention-LSTM model has better model performance and can provide effective help for traffic management control and traffic flow theory research.

An Effective Self-Attention-Based Hybrid Model for Short-Term Traffic Flow Prediction

Vehicle exhaust is one of the main sources of carbon emissions. The short-term traffic flow prediction plays an important role in alleviating traffic congestion, optimizing the travel structure, and reducing traffic carbon emissions. The current advanced models of short-term traffic flow prediction are evaluated in this work, especially their inadequacies. To improve the prediction accuracy and ensure fine traffic management, an effective self-attention-based hybrid model is proposed to predict the short-term traffic flow. The proposed model includes an encoder-decoder neural network module and a self-attention mechanism module. The self-attention mechanism module is applied as a feature extraction unit in this hybrid model to enhance the ability of key information capture and to settle the problem on key information disappearing due to the increasing sequence length in traditional models. The dataset of the Guangdong freeway toll station is used for the experimental testing. Compared with several baseline models, the proposed model is more suitable for real-time prediction and can provide highly accurate results. Also, a better interpretability is presented in this proposed model. The experimental results showed that MAE, RMSE, and MAPE of the proposed model are 3.01, 4.38, and 12.99%, respectively. Our new hybrid model gives a higher accuracy than the support vector regression (SVR) model, LSTM neural network-attention (LSTM-attention) model, and temporal convolutional network (TCN) model. It shows that the proposed model in this work is favorable to the short-term traffic flow prediction.

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In this paper, we have proposed a novel hybrid algorithm utilizing Principal Component Analysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory (LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCA removes unwanted information from the dataset and selects essential features. Secondly, SAE is used to reduce the dimension of input data using one-hot encoding so the model can be trained with better speed. Thirdly, LSTM takes the input from SAE, where the data is sorted in ascending order based on the important features and generates the derived value. Finally, KNN Regressor takes information from LSTM to predict traffic flow. The forecasting performance of the PALKNN model is investigated with Open Road Traffic Statistics dataset, Great Britain, UK. This paper enhanced the traffic flow prediction for every hour of a day with a minimal error value. An extensive experimental analysis was performed on the benchmark dataset. The evaluated results indicate the significant improvement of the proposed PALKNN model over the recent approaches such as KNN, SARIMA, Logistic Regression, RNN, and LSTM in terms of root mean square error (RMSE) of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE) of 2.04%.

Spatio-Temporal vehicle traffic flow prediction using multivariate CNN and LSTM model

Traffic congestion is a major problem in developing and developed countries vehicle traffic management systems. Traffic control system works based on the idea of removing instabilities and avoid accidents in order to minimize the traffic and maximize the vehicle flow. To control the congestion need to predict the upcoming traffic flow and it will be useful for Advanced Traffic Information Systems (ATIS), Advanced Traffic Management Systems (ATMS) and traffic analytics. Non–linear historical data and uncertain factors influence the vehicle congestion at peak hours which cannot be considered in existing algorithms. This study proposes hybrid neural network algorithms such as Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) network for short term traffic flow prediction based on multivariate analysis. Widely referred datasets Performance Measurement Systems (PEMS) and Mesowest have been used to evaluate this model. Experiment results shows that CNN-LSTM Hybrid prediction model achieves high accuracy compared with other models.

Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning

This study proposes a short-term traffic flow prediction model that combines community detection-based federated learning with a graph convolutional network (GCN) to alleviate the time-consuming training, higher communication costs, and data privacy risks of global GCNs as the amount of data increases. The federated community GCN (FCGCN) can achieve timely, accurate, and safe traffic state predictions in the era of big traffic data, which is critical for the efficient operation of intelligent transportation systems. The FCGCN prediction process has four steps: dividing the local subnetwork with community detection, local training based on the global parameters, uploading the local model parameters, and constructing a global model prediction based on the aggregated parameters. Numerical results on the PeMS04 and PeMS08 datasets show that the FCGCN outperforms four benchmark models, namely, the long short-term memory (LSTM), convolutional neural network (CNN), ChebNet, and graph attention network (GAT) models. The FCGCN prediction is closer to the real value, with nearly the same performance as the global model at a lower time cost, thus achieving accurate and secure short-term traffic flow predictions with three parameters: flow, speed, and occupancy.

Short-Term Traffic Flow Prediction Based on VMD and IDBO-LSTM

Short-Term Traffic Flow Prediction Based on VMD and IDBO-LSTM

Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning

Traffic flow has the characteristics of randomness, complexity, and nonlinearity, which brings great difficulty to the prediction of short-term traffic flow. Based on considering the advantages and disadvantages of various prediction models, this paper proposes a short-term traffic flow prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and machine learning. Firstly, CEEMDAN is used to decompose the original traffic flow time series to obtain multiple component sequences with huge complexity differences. In order to measure the complexity of each component sequence, the permutation entropy of each component sequence is calculated. According to the permutation entropy, the component sequence is divided into three types: high-frequency components, intermediate-frequency components, and low-frequency components. Secondly, according to the different volatility of the three types of components, the high-frequency components, intermediate-frequency components, and low-frequency components are predicted by long short-term memory (LSTM), support vector machine (SVM), and k-nearest neighbor (KNN), respectively. Finally, the accurate traffic flow prediction value can be obtained by the linear superposition of the prediction results of the three component prediction models. Through a measured traffic flow data, the combined model proposed in this paper is compared to the binary gray wolf algorithm–long short-term memory (BGWO-LSTM) model, the improved gray wolf algorithm–support vector machine (IGWO-SVM) model, and the KNN model. The mean square error (MSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 41.26, 44.98, and 57.69, respectively. The mean absolute error (MAE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.33, 2.44, and 2.70, respectively. The root mean square error (RMSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.89, 3.11, and 3.80, respectively. The three error indexes of the combined model are far smaller than those of the other single models. At the same time, the decision coefficient (R2) of the combined model is also closer to 1 compared to the other models, indicating that the prediction result of the combined model is the closest to the actual traffic flow.

Retraction Note: Research on Emergency Vehicle Routing Planning Based on Short-Term Traffic Flow Prediction

Retraction Note: Research on Emergency Vehicle Routing Planning Based on Short-Term Traffic Flow Prediction

Partial differential grey model based on control matrix and its application in short-term traffic flow prediction

Short-term traffic flow analysis is the core part of the intelligent transportation system, and also the critical basis for traffic management and control system to guide traffic flow. Real-time and accurate short-term traffic flow prediction can be effectively applied to traffic control and guidance, providing reliable and effective information for traffic managers and travelers, and improving travel efficiency. Based on the spatiotemporal characteristics of traffic flow and periodicity of traffic flow, this paper uses partial differential equation variables to reflect the spatiotemporal characteristics of traffic flow, combines the modelling mechanism of partial differential and the grey prediction model organically, and establishes a partial grey prediction model with control matrix by using the partial derivatives of the mean sequence. At the same time, the parameters estimation and modelling steps of the novel model are obtained by matrix analysis and recursion. A set of data is used from two perspectives to verify the proposed model's effectiveness. Experimental results show that the proposed model is stable and effective. Finally, the novel model is applied to the short-time traffic flow simulation and prediction of the highway. The research results show that the novel model is better than other comparison models. The novel model mainly aims at the matrix data form of traffic flow and is applied to general short-term traffic flow prediction. On the one hand, the prediction results of the novel model can provide theoretical basis and basic methods for the traffic management system, traffic data for traffic information system, and new ideas for the traffic flow prediction research. On the other hand, it can help traffic managers solve accurate real-time traffic flow information, provide real-time traffic information for travelers, and help them plan and adjust routes in advance.