Short-term Traffic Flow Prediction Research Articles

Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction.

Considering that the road short-term traffic flow has strong time series correlation characteristics, a new long-term and short-term memory neural network (LSTM)-based prediction model optimized by the improved genetic algorithm (IGA) is proposed to improve the prediction accuracy of road traffic flow. Firstly, an improved genetic algorithm (IGA) is proposed by dynamically adjusting the mutation rate and crossover rate of standard GA. Secondly, the parameters of the LSTM, such as the number of hidden units, training times, gradient threshold and learning rate, are optimized by the IGA. Therefore, the optimal parameters are obtained. In the analysis stage, 5-min short-term traffic flow data are used to demonstrate the superiority of the proposed method over the existing neural network algorithms. Finally, the results show that the Root Mean Square Error achieved by the proposed algorithm is lower than that achieved by the other neural network methods in both the weekday and weekend data sets. This verifies that the algorithm can adapt well to different kinds of data and achieve higher prediction accuracy.

Short-Term Traffic Flow Prediction via Improved Mode Decomposition and Self-Attention Mechanism Based Deep Learning Approach

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

Highway Traffic Flow Prediction Model Construction Based on the Gray Theory and BP Neural Network.

The short-term traffic flow prediction and modeling of highways are the core content and important foundation of highway management decision-making support systems. It is of great significance to improving the level of highway management. Based on the macrodynamic traffic flow model, this article establishes a method for establishing a highway traffic flow prediction model based on the BP neural network theory and gray theory. We collected and carry out modeling and prediction of highway traffic flow data near a certain station. It is learned from the prediction results that the traffic flow prediction model based on the BP neural network and gray theory has a high degree of reliability.

Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient

The prediction of short-term traffic flow is critical for improving service levels for drivers and passengers as well as enhancing the efficiency of traffic management in the urban transportation system. For transportation departments, the issue remains of how to efficiently utilize the spatial and temporal information of traffic data for better prediction performance. As a means of improving traffic prediction accuracy, this paper proposes a method for screening spatial time-delayed traffic series based on the maximal information coefficient. The selected time-delayed traffic series are transformed into traffic state vectors, from which traffic flow is predicted by adopting the combination of support vector regression method and k-nearest neighbors method. We employ the proposed framework for real-world traffic flow prediction. Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE) validate the superior performance of the proposed model compared to traditional methods. This new approach reduces the RMSE by 23.448% and the MAPE by 14.726% of the predicted results.

Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction.

Three different types of entropy weight methods (EWMs), i.e., EWM-A, EWM-B, and EWM-C, have been used by previous studies for integrating prediction models. These three methods use very different ideas on determining the weights of individual models for integration. To evaluate the performances of these three EWMs, this study applied them to developing integrated short-term traffic flow prediction models for signalized intersections. At first, two individual models, i.e., a k-nearest neighbors (KNN)-algorithm-based model and a neural-network-based model (Elman), were developed as individual models to be integrated using EWMs. These two models were selected because they have been widely used for traffic flow prediction and have been approved to be able to achieve good performance. After that, three integrated models were developed by using the three different types of EWMs. The performances of the three integrated models, as well as the individual KNN and Elman models, were compared. We found that the traffic flow predicted with the EWM-C model is the most accurate prediction for most of the days. Based on the model evaluation results, the advantages of using the EWM-C method were deliberated and the problems with the EWM-A and EWM-B methods were also discussed.

Stable Computation of Least Squares Problems of the OGM(1,N) Model and Short-Term Traffic Flow Prediction

Stable Computation of Least Squares Problems of the OGM(1,N) Model and Short-Term Traffic Flow Prediction

Short-Term Traffic Flow Prediction of Expressway Considering Spatial Influences

Real-time and accurate short-term traffic flow prediction is important for the operation and management of expressways. This paper presents a hybrid model that can be used to discover the spatiotemporal dependencies of traffic flows and, thus, achieve a more accurate traffic flow forecast. This model stacks a full connection (FC) layer, two-layer long short-term memory (LSTM), and a middle mean pooling layer, denoted by FC-LSTM, to expand the ability of LSTM to capture spatial correlations and too long-term temporal dependencies of traffic flows. Traffic data of 51 toll stations (accounting for 15% of all stations) in the Guizhou expressway network in China in January 2016 were used to evaluate the validation of FC-LSTM. The results showed that the traffic flows at adjacent tollgates were interactional and correlated, that FC-LSTM could capture this spatial dependency and the temporal correlations of traffic flows, and, thus, that it was superior to other baselines with a low prediction error, high precision, and high fitting degree. Moreover, FC-LSTM is interpretable and robust owing to its explicit input and is suitable for traffic flow prediction for most tollgates under the same parameters.

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 Multifeature Fusion Short-Term Traffic Flow Prediction Model Based on Deep Learnings

Short-term traffic flow prediction is an important component of intelligent transportation systems, which can support traffic trip planning and traffic management. Although existing predicting methods have been applied in the field of traffic flow prediction, they cannot capture the complex multifeatures of traffic flows resulting in unsatisfactory short-term traffic flow prediction results. In this paper, a multifeature fusion model based on deep learning methods is proposed, which consists of three modules, namely, a CNN-Bidirectional GRU module with an attention mechanism (CNN-BiGRU-attention) and two Bidirectional GRU modules with an attention mechanism (BiGRU-attention). The CNN-BiGRU-attention module is used to extract local trend features and long-term dependent features of the traffic flow, and the two BiGRU-attention modules are used to extract daily and weekly periodic features of the traffic flow. Moreover, a feature fusion layer in the model is used to fuse the features extracted by each module. And then, the number of neurons in the model, the loss function, and other parameters such as the optimization algorithm are discussed and set up through simulation experiments. Finally, the multifeature fusion model is trained and tested based on the training and test sets from the data collected from the field. And the results indicate that the proposed model can better achieve traffic flow prediction and has good robustness. Furthermore, the multifeature fusion model is compared and analyzed against the baseline models with the same dataset, and the experimental results show that the multifeature fusion model has superior predictive performance compared to the baseline models.

Short-Term Traffic Flow Prediction with Structure-Optimized Deep Belief Network

This paper proposes a structure-optimized deep belief network method for short-term traffic flow forecast, which is used to solve the problems of too simple training data in deep learning short-term traffic flow forecast and random selection of model structure construction parameters. We constructed a deep belief network short-term traffic flow forecast model that can simultaneously train three types of traffic data related to the predicted node traffic volume, enhance the spatiotemporal correlation of predictions, and overcome the shortcomings of too single training data. At the same time, we optimize the short-term traffic flow prediction model structure of the deep belief network; and use the T-PSO algorithm to optimize the hidden layer structure parameters of the prediction model. It can avoid the decrease in the practicability of the model caused by the random selection of the model structure construction parameters. The experimental results show that the method of structure-optimized deep belief network is feasible and effective, and the prediction accuracy is better than the classic deep learning prediction model.

A Spatio-Temporal Graph Neural Network Approach for Traffic Flow Prediction

The accuracy of short-term traffic flow prediction is one of the important issues in the construction of smart cities, and it is an effective way to solve the problem of traffic congestion. Most previous studies could not effectively mine the potential relationship between the temporal and spatial dimensions of traffic data flow. Due to the large variability in the traffic flow data of road conditions, we analyzed it with “dynamic”, using a dynamic-aware graph neural network model for the hidden relationships between space-time in the deep learning segment. In this paper, we propose a dynamic perceptual graph neural network model for the temporal and spatial hidden relationships of deep learning segments. This model mixes temporal features and spatial features with graphs and expresses them. The temporal features and spatial features are connected to each other to learn potential relationships, so as to more accurately predict the traffic speed in the future time period, we performed experiments on real data sets and compared with some baseline models. The experiments show that the method proposed in this paper has certain advantages.

A traffic flow forecasting method based on the GA-SVR

This paper uses support vector regression (SVR) to predict short-term traffic flow, and studies the feasibility of SVR in short-term traffic flow prediction. The short-time traffic flow has many influencing factors, which are characterized by nonlinearity, randomness and periodicity. Therefore, SVR algorithm has advantages in dealing with such problems. In order to improve the prediction accuracy of the SVR, this paper uses genetic algorithm (GA) to optimize the SVR and other parameters to obtain the global optimal solution. The optimal parameters are used to construct the SVR prediction model. This paper selects the traffic flow data of the Jiangxi Provincial Transportation Department database to verify the feasibility and effectiveness of the proposed model.

A Deep Learning–Based Approach for Moving Vehicle Counting and Short-Term Traffic Prediction From Video Images

The intelligent transportation system (ITS) is one of the effective solutions to the problem of urban traffic congestion, and it is also one of the important topics of smart city construction. One particular application is the traffic monitoring and flow prediction. However, there are still challenges regarding both aspects. On the one hand, the current traffic monitoring relies heavily on the single object detection method that cannot achieve accurate statistics of moving target counting and, meanwhile, has limited speed advantage; on the other hand, the existing traffic flow prediction models rarely consider different weather conditions. Therefore, the present article attempts to propose a packaged solution, which combines a new target tracking and moving vehicle counting method and an improved long short-term memory (LSTM) network for traffic flow forecast with weather conditions. More specifically, the DCN V2 convolution kernel and MultiNetV3 framework are used to replace YOLOv4’s conventional convolution kernel and backbone network to realize multi-target tracking and counting, respectively. Subsequently, combined with the temporal characteristics of historical traffic flow, this article introduces weather conditions into the LSTM network and realizes the short-term prediction of traffic flow at the road junction level. This study carries out a series of experiments using the real traffic video data with a 2-month time span at a popular road junction in the downtown of Shenzhen, China. The results suggest that the proposed algorithms outperform the previous methods in terms of the 10% higher accuracy of target detection tracking and about a half reduction of traffic prediction error, when considering weather conditions.

An Improved CEEMDAN-FE-TCN Model for Highway Traffic Flow Prediction

With the advent of the data-driven era, deep learning approaches have been gradually introduced to short-term traffic flow prediction, which plays a vital role in the Intelligent Transportation System (ITS). A hybrid predicting model based on deep learning is proposed in this paper, including three steps. Firstly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is applied to decompose the nonlinear time series of highway traffic flow to obtain the intrinsic mode function (IMF). The fuzzy entropy (FE) is then calculated to recombine subsequences, highlighting traffic flow dynamics in different frequencies and improving prediction efficiency. Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. Two sensors of US101-S on the main road and on-ramp were selected to measure the prediction effect. The results show that the prediction error of the proposed model on two sensors is notably decreased on single-step and multistep prediction, compared with the original TCN model. Furthermore, the proposed improved CEEMDAN-FE-X framework can be combined with prevailing prediction methods to increase the prediction accuracy, among which the improved CEEMDAN-FE-TCN model has the best performance and strong robustness.

Robust-LSTM: a novel approach to short-traffic flow prediction based on signal decomposition

Intelligent transport systems need accurate short-term traffic flow forecasts. However, developing a robust short-term traffic flow forecasting approach is a challenging task due to the stochastic character of traffic flow. This study proposes a novel approach for short-term traffic flow prediction task, namely Robust Long Short Term Memory (R-LSTM) based on Robust Empirical Mode Decomposing (REDM) algorithm and Long Short Term Memory (LSTM). Short-term traffic flow data provided from the Caltrans Performance Measurement System (PeMS) database were used in the training and testing of the model. The dataset was composed of traffic data collected by 25 traffic detectors on different freeways’ main lanes. The time resolution of the dataset was set to 15 min, and the Hampel preprocessing algorithm was applied for outlier elimination. The R-LSTM predictions were compared with the state-of-the-art models, utilizing RMSE, MSE, and MAPE as performance criteria. Performance analyses for various periods show that R-LSTM is remarkably successful in all time periods. Moreover, developed model performance is significantly higher, especially during midday periods when traffic flow fluctuations are high. These results show that R-LSTM is a strong candidate for short-term traffic flow prediction, and can easily adapt to fluctuations in traffic flow. In addition, robust models for short-term predictions can be developed by applying the signal separation method to traffic flow data.

Robust ensemble method for short-term traffic flow prediction

Accurate and real-time prediction of short-term traffic flow plays an increasingly vital role in the successful deployment of Intelligent Transportation Systems. Although existing studies have been done for traffic flow prediction problem, their efficacy relies heavily on traffic data. However, collected traffic data are usually affected by various external factors (e.g, weather, traffic jams and accidents), leading to errors and missing data. This makes it difficult to pick a single method that works well all the time. This paper concentrates on investigating ensemble learning that benefits from multiple base methods and presents an effective and robust ensemble method by using the bagging ensemble technique averaging to improve the traffic flow prediction performance. To enhance the robustness of constructed ensemble method, three improved least squares twin support vector regression methods are proposed based on robust L1-norm, L2,p-norm and Lp-norm distance to alleviate the negative effect of traffic data with outliers. In addition, a pruning scheme is utilized to remove anomalous individual components. This makes the proposed method more effective for traffic flow prediction. Further, a comprehensive traffic flow indicator system based on speed, traffic volume, occupancy and ample degree is utilized to forecast the traffic flow. To promote the prediction performance, we optimize the parameters of each component in ensemble method with the adaptive particle swarm optimization. The results on real traffic data demonstrate that the proposed ensemble method yields better prediction performance and robustness even when the standalone components and other competitors make unsatisfactory predictions.

Short-Term Traffic Flow Prediction and Signal Timing Optimization Based on Deep Learning

The rapid development of traffic industry has caused traffic congestion and environmental pollution and other problems. Traffic flow prediction and signal timing optimization under different road environments can improve the current traffic environment. In this paper, based on historical traffic data and BP neural network, according to the characteristics of different deep learning modes, SVM neural network is integrated to achieve accurate prediction of traffic flow, fully analyze the characteristics of traffic flow in different traffic environments, and accurately estimate the current traffic capacity. Meanwhile, on the basis of traffic flow prediction data and fuzzy control method, the signal timing optimization of three-phase intersections is realized to ensure the maximum capacity and the lowest average delay time of intersections. Simulation experiments show that the accuracy of the traffic flow prediction method designed in this paper is about 19.5% higher than that of the traditional mean prediction method, and fusion neural network model is more accurate than single neural network model. The optimized signal timing method can accurately control the traffic process with different phases and has obvious improvement in traffic capacity and average delay time compared with the traditional method.

Stacked LSTM for Short-Term Traffic Flow Prediction using Multivariate Time Series Dataset

Stacked LSTM for Short-Term Traffic Flow Prediction using Multivariate Time Series Dataset

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

To improve the management ability of expressway service area and formulate strategies for traffic flow changes in time, a short-term traffic flow prediction model is proposed. Firstly, cleaning the extracted data according to the rules and constructing four kinds of features (temporal, spatial, statistical and external factors). Then, a short-term traffic flow prediction model WADNN (wide attention and deep neural networks) is constructed. In the model, LSTM (long and short-term memory neural network), CNN (convolution neural network) and self-attention mechanism are used to extract different features respectively. In addition, the STL (Seasonal-Trend decomposition procedure based on LOESS) algorithm is used to decompose the traffic flow to fit the trend better. For the three decomposed components, the OMS (optimal model selection) operation is carried out, the prediction of each component is added to obtain the final predicted value, and the model effect is measured according to the RMSE (root mean square error), MAE (mean absolute error), MAPE (Mean Absolute Percentage Error) and R2 coefficient. Finally, taking an expressway service area as an example, the proposed model is compared with some common models. The results show that the prediction effect of WADNN is better and STL-OMS can further improve the accuracy.

Prediction model for short‐term traffic flow based on a K‐means‐gated recurrent unit combination

Short-term forecasting of traffic flow is an indispensable part of easing traffic pressure. Considering that different traffic flow patterns will affect the short-term traffic flow prediction results, a combined method based on the K-means clustering algorithm and gated recurrent unit (GRU) is proposed to build a short-term traffic flow prediction model to overcome the above problems. The K-means algorithm is used to cluster historical traffic flow data to establish different traffic flow pattern libraries. The K-nearest neighbour (KNN) classification algorithm is used to determine the historical traffic flow pattern most similar to the traffic flow change trend of the date to be predicted. All historical traffic flow data in this category is used training samples to make targeted predictions. The traffic flow data of performance measurement system (PeMS) in California, USA is used to verify the performance of the proposed model. Compared with the GRU network, stacked auto encoders (SAEs), random forest (RF), and support vector machine regression (SVR), the results show that the proposed combination model K-means-GRU considers the diversity of traffic flow patterns and improves the prediction accuracy, it can better solve the short-term traffic flow prediction problem.