Short-time Traffic Flow Prediction Research Articles

Survey of short-term traffic flow prediction based on LSTM

Short-time traffic flow prediction can not only help urban traffic management to complete the control and induce but also reduce the degree of urban congestion and improve the efficiency of urban operation. At the same time, improving the effect of short-time traffic flow prediction is one of the key points of traffic control and guidance development. Currently, there are many different models for short-term traffic flow prediction, and different prediction models have their different advantages and disadvantages. In this paper, we review the current status of research on short-time traffic flow prediction based on Long Short-Term Memory (LSTM) neural network: first, we analyze the research method in this paper; second, we briefly review the research and application effects of common prediction methods in traffic flow prediction; finally, the optimization method based on LSTM and its application effect are summarized and analyzed from the aspect of LSTM network and its combination model. In addition, short-time traffic flow prediction still faces challenges due to the problems of the data itself and the complexity of real-world traffic flow impact factors.

Application of Autoformer to Short-Term Traffic Flow Prediction

This paper investigates the application of Autoformer model in the field of short-term traffic flow prediction. Traffic flow prediction is crucial for urban planning and traffic management, and is important for relieving traffic congestion and improving traffic efficiency. Autoformer, as a Transformer model based on the self-attention mechanism, provides a powerful modeling capability for the task of traffic flow prediction through its ability to automatically learn feature relationships. The paper first introduces the background and importance of traffic flow prediction and outlines current prediction methods and their limitations. Subsequently, the structure and working principle of Autoformer are described in detail, elucidating the advantages over traditional models. Autoformer’s self-attention mechanism can effectively capture the long-range dependencies in the input sequences and better adapt to the dynamic changes of traffic flow. To verify the performance of Autoformer in short-time traffic flow prediction, experiments are conducted using real traffic flow datasets. The results show that Autoformer significantly improves the prediction accuracy and generalization ability compared to traditional time series models. In addition, model interpretive analysis was conducted, revealing the advantages of Autoformer for automatic extraction of traffic flow features and correlation modeling.

A multi-source data fusion model for traffic flow prediction in smart cities

Abstract The emergence of problems such as increased urban traffic and transportation, traffic congestion, and road resource shortages prompted the city to prioritize the construction of intelligent transportation. Intelligent computing technology provides technical support for people’s smooth travel, as well as for the construction and development of the city. The article first delves into the basic theory of support vector machines, outlines a specific process for traffic prediction using these machines, and then suggests a method for preprocessing traffic data. This method primarily involves three steps: data collection, abnormal data elimination, and missing data recovery. Finally, it proposes a traffic flow prediction model that utilizes multi-source data from support vector machines. The experimental results demonstrate a higher degree of consistency between the prediction results and the actual results in the analysis of short-time traffic flow prediction on weekdays and weekends. Furthermore, the traffic flow prediction model based on Support Vector Machine, proposed in this paper, is capable of reliably predicting vehicular traffic flow in various severe weather environments, with a coefficient of parity below 0.018.

A novel grey prediction model based on tensor higher-order singular value decomposition and its application in short-term traffic flow

Short-term traffic flow prediction is the key to traffic guidance and control and can directly affect the performance of intelligent transportation systems. Traffic flow data has the characteristics of volatility, chaos, and randomness, which affect the accuracy of the prediction model. Based on the multi-dimensional spatio-temporal data characteristics of traffic flow data, this paper combines the tensor higher-order singular value decomposition theory and the modeling mechanism of the classical grey model GM(1,1) model and establishes the grey GM(1,1) model with tensor higher-order singular values. The tensor higher-order singular value decomposition reflects the periodic, multi-modal, and holistic nature of traffic flow data, which can mitigate the volatility and randomness of traffic flow data and improve the accuracy of the model. Then, the new model is applied to highway short-time traffic flow prediction, analyzing the spatio-temporal nature of traffic flow data, giving the detailed steps of model modeling, and analyzing the correlation between the original traffic flow data and tensor approximation data using grey correlation degrees. There are three cases to illustrate the effectiveness of the model. Case 1 shows that the results of MAPE from nine modeling objects are stable at about 5%, which indicates that the new model has some stability; Case 2 shows that the new model is more adaptable to short-time traffic flow prediction based on the results of three different modeling and prediction objects; Case 3 uses the new model to compare it with two traditional grey forecasting models and two optimization models, and the results indicate that the new model has a total MAPE value of 5.172%, which is better than the other four grey forecasting models. Finally, the new model is applied to short-time traffic flow prediction, and its prediction results are consistent with the trend of the original traffic flow data, indicating that it can reveal the real-time characteristics of the traffic system to a certain extent and provide a reliable theoretical basis for traffic planning, control, and optimization.

A novel traffic optimization method using GRU based deep neural network for the IoV system.

At present, China is moving towards the direction of "Industry 4.0". The development of the automobile industry, especially intelligent automobiles, is in full swing, which brings great convenience to people's life and travel. However, at the same time, urban traffic pressure is also increasingly prominent, and the situation of traffic congestion and traffic safety is not optimistic. In this context, the Internet of Vehicles (also known as "IoV") opens up a new way to relieve urban traffic pressure. Therefore, in order to further optimize the road network traffic conditions in the IoV environment, this research focuses on the traffic flow prediction algorithm on the basis of deep learning to enhance traffic efficiency and safety. First, the study investigates the short-time traffic flow prediction by combining the characteristics of the IoV environment. To address the issues that existing algorithms cannot automatically extract data features and the model expression capability is weak, the study chooses to build a deep neural network using GRU model in deep learning for short-time traffic flow prediction, thereby improving the prediction accuracy of algorithm. Secondly, a fine-grained traffic flow statistics approach suitable for the IoV situation is suggested in accordance with the deep learning model that was built. The algorithm sends the vehicle characteristic data obtained through GRU model training into the fine-grained traffic flow statistics algorithm, so as to realize the statistics of traffic information of various types of vehicles. The advantage of this algorithm is that it can well count the traffic flow of multiple lanes, so as to better predict the current traffic status and achieve traffic optimization. Finally, the IoV environment is constructed to confirm the effectiveness of the prediction model. The prediction results prove that the new algorithm has good performance in traffic flow statistics in different scenarios.

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.

A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors

Accurate short-term traffic flow prediction is a prerequisite for achieving an intelligent transportation system to proactively alleviate traffic congestion. Considering the complex and variable traffic environment, so that the traffic flow contains a large number of non-linear characteristics, which makes it difficult to improve the prediction accuracy, a combined prediction model that reduces the unsteadiness of traffic flow and fully extracts the traffic flow features is proposed. Firstly, decompose the traffic flow data into multiple components by the seasonal and trend decomposition using loess (STL); these components contain different features, and the optimized variational modal decomposition (VMD) is used for the second decomposition of the component with large fluctuation frequencies, and then the components are reconstructed according to the fuzzy entropy and Lempel-Ziv complexity index and the Pearson correlation coefficient is used to filter the traffic flow features. Then light gradient boosting machine (LightGBM), long short-term memory with attention mechanism (LA), and kernel extreme learning machine with genetic algorithm optimization (GA-KELM) are built for prediction. Finally, we use reinforcement learning to integrate the advantages of each model, and the weights of each model are determined to obtain the best prediction results. The case study shows that the model established in this paper is better than other models in predicting urban road traffic flow, with an average absolute error of 2.622 and a root mean square error of 3.479, both of which are lower than the prediction errors of other models, indicating that the model can fully extract the features in complex traffic flow.

Short-Term Traffic-Flow Forecasting Based on an Integrated Model Combining Bagging and Stacking Considering Weight Coefficient

This work proposed an integrated model combining bagging and stacking considering the weight coefficient for short-time traffic-flow prediction, which incorporates vacation and peak time features, as well as occupancy and speed information, in order to improve prediction accuracy and accomplish deeper traffic flow data feature mining. To address the limitations of a single prediction model in traffic forecasting, a stacking model with ridge regression as the meta-learner is first established, then the stacking model is optimized from the perspective of the learner using the bagging model, and lastly the optimized learner is embedded into the stacking model as the new base learner to obtain the Ba-Stacking model. Finally, to address the Ba-Stacking model’s shortcomings in terms of low base learner utilization, the information structure of the base learners is modified by weighting the error coefficients while taking into account the model’s external features, resulting in a DW-Ba-Stacking model that can change the weights of the base learners to adjust the feature distribution and thus improve utilization. Using 76,896 data from the I5NB highway as the empirical study object, the DW-Ba-Stacking model is compared and assessed with the traditional model in this paper. The empirical results show that the DW-Ba-Stacking model has the highest prediction accuracy, demonstrating that the model is successful in predicting short-term traffic flows and can effectively solve traffic-congestion problems.

Effect of Multi-Scale Decomposition on Performance of Neural Networks in Short-Term Traffic Flow Prediction

Numerous studies employ multi-scale decomposition to improve the prediction performance of neural networks, but the grounds for selecting the decomposition algorithm are not explained, and the effects of decomposition algorithms on other performance of neural networks are also lacking further study. This paper studies the influence of commonly used multi-scale decomposition algorithms including EMD (Empirical Mode Decomposition), EEMD(Ensemble Empirical Mode Decomposition), CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise), VMD (Variational Mode Decomposition), WD (Wavelet Decomposition), and WPD (Wavelet Packet Decomposition) on the performance of Neural Networks. Decomposition algorithms are adopted to decompose traffic flow data into component signals, and then K-means is used to cluster component signals into volatility components, periodic components, and residual components. A Bi-directional LSTM (BiLSTM) neural network is adopted as the standard model for training and forecasting. Finally, three metrics, including prediction performance, robustness, and generalization performance are proposed to evaluate the influence of the multi-scale decomposition algorithm for neural networks comprehensively. By comparing the evaluation results of different hybrid models, this study provides some useful suggestions on proper multi-scale decomposition algorithm selection in short-time traffic flow prediction.

A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction

Accurate short-time traffic flow prediction has gained gradually increasing importance for traffic plan and management with the deployment of intelligent transportation systems (ITSs). However, the existing approaches for short-term traffic flow prediction are unable to efficiently capture the complex nonlinearity of traffic flow, which provide unsatisfactory prediction accuracy. In this paper, we propose a deep learning based model which uses hybrid and multiple-layer architectures to automatically extract inherent features of traffic flow data. Firstly, built on the convolutional neural network (CNN) and the long short-term memory (LSTM) network, we develop an attention-based Conv-LSTM module to extract the spatial and short-term temporal features. The attention mechanism is properly designed to distinguish the importance of flow sequences at different times by automatically assigning different weights. Secondly, to further explore long-term temporal features, we propose a bidirectional LSTM (Bi-LSTM) module to extract daily and weekly periodic features so as to capture variance tendency of the traffic flow from both previous and posterior directions. Finally, extensive experimental results are presented to show that the proposed model combining the attention Conv-LSTM and Bi-LSTM achieves better prediction performance compared with other existing approaches.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

Short-term traffic flow prediction is an important part of intelligent transportation systems research and applications. For further improving the accuracy of short-time traffic flow prediction, a novel hybrid prediction model (multivariate phase space reconstruction–combined kernel function-least squares support vector machine) based on multivariate phase space reconstruction and combined kernel function-least squares support vector machine is proposed. The C-C method is used to determine the optimal time delay and the optimal embedding dimension of traffic variables’ (flow, speed, and occupancy) time series for phase space reconstruction. The G-P method is selected to calculate the correlation dimension of attractor which is an important index for judging chaotic characteristics of the traffic variables’ series. The optimal input form of combined kernel function-least squares support vector machine model is determined by multivariate phase space reconstruction, and the model’s parameters are optimized by particle swarm optimization algorithm. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. The experimental results suggest that the new proposed model yields better predictions compared with similar models (combined kernel function-least squares support vector machine, multivariate phase space reconstruction–generalized kernel function-least squares support vector machine, and phase space reconstruction–combined kernel function-least squares support vector machine), which indicates that the new proposed model exhibits stronger prediction ability and robustness.

An Improved Double-layer K-nearest Neighbor Nonparametric Regression Method for Short-time Traffic Flow Prediction

In combination with the repeatability of the traffic flow state patterns, this article improved the k-nearest neighbor non-parametric regression method. To be specific, the neighbors were screened twice and the function based on state pattern recognition was introduced; moreover, the traffic flows in the past time and the traffic flows towards the related directions at both upstream and downstream crossroads were taken into account, so that the predictive ability of the proposed k-nearest neighbor non-parametric regression method can be improved. In addition, the final prediction results were output using the weighted average method of the reciprocal of the state pattern vector matching distance, so as to enhance the accuracy and real-time performance of the short-term traffic flow prediction.

Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods.

Short time traffic flow prediction based on Volterra model using multiplication-coupled configuration

An adaptive third-order Volterra filter for short-time traffic flow prediction was proposed,which was based on intrinsic nonlinearity and deterministic mechanism of chaotic time series and nonlinear expression of Volterra series.Concerning the problem that the coefficients of Volterra filter exponentially increase with its order,an approximate multiplication-coupled structure for Volterra filter was studied.At first,through properly choosing time delay and embedding dimension using mutual information method and false nearest neighbor method,respectively,the largest Lyapunov exponent was estimated by applying small data sets method so as to validate that chaos existed in traffic flow series.Then,the approximate multiplication-coupled structure for third-order Volterra filter,of which coefficients were adaptively adjusted by using an improved nonlinear Normalized Least Mean Square(NLMS) algorithm,was employed to reduce computational complexity.Finally,by applying the proposed method to the real measured traffic flow data,the experimental results show that chaos exists in traffic flow series and the proposed scheme can effectively predict traffic flow series and reduce model complexity.

Short-term traffic flow prediction for multi traffic states on urban expressway network

Short-term traffic flow prediction for multi traffic states on urban expressway network is carried out in this paper. The model for multi traffic states is proposed by integrating the spatial and the temporal distribution characteristics of traffic flow parameters under free traffic, congested traffic and jam traffic respectively. Based on the classical traffic flow conservation equation, the ideology of spatial and temporal dispersions in partial differential equations is adopted to establish short-time traffic flow prediction model. Meanwhile, the impact factors, such as on and off ramp, lane change and road slope are considered, which convertes short-term traffic flow prediction model into short-time traffic flow prediction state space model. Finally, the short-term traffic flow prediction for multi traffic states on urban expressway network is realized. The empirical research shows that compared with the classic ARMA model, the proposed method can not only realize short-term traffic flow prediction for multi traffic states on urban expressway network but also achieve an accuracy of 90.23%. In the same condition, the accuracy of ARMA model is 81%.

Novel Model Based on Wavelet Transform and GA-fuzzy Neural Network Applied to Short Time Traffic Flow Prediction

Novel Model Based on Wavelet Transform and GA-fuzzy Neural Network Applied to Short Time Traffic Flow Prediction

Improved Hybrid Intelligent Method for Urban Road Traffic Flow Forecasting based on Chaos-PSO Optimization

Real time traffic flow is often difficult to predict precisely because of the nonlinear and stochastic characteristics of the traffic flow data. Intelligent prediction methods such as artificial neural network (ANN), support vector machine (SVM), etc. have been proven effective to discover the nonlinear information hidden in the traffic flow data. Nevertheless, their efficiency limits in the low recognition rate. If using them independently, the prediction accuracy would be lower than integrated use. Hence, a new hybrid intelligent prediction approach base on the combination of advanced signal processing technique and intelligent data mining method is proposed for the short time traffic flow prediction in this paper. The new method is marked as the use of empirical mode decomposition (EMD) and support vector machine (SVM) to deal with the nonlinear and stochastic characteristics of the traffic flow data. Another advantage of the proposed method is that the Chaos-particle swarm optimization (PSO) is adopted for the optimization of the combination. By doing so, the local optimization of the EMD-SVM prediction model can be avoided and the forecasting rate can be enhanced significantly. The practical traffic flow data were applied to the validation of the proposed prediction model. The analysis results show that the proposed method can extract the underlying rules of the testing data and decrease prediction error by 0.53% or better when compared with single SVM approach. Thus, the new hybrid intelligent traffic flow forecasting model can provide practical application.

The Model of Short-Time Traffic Flow Prediction on High-Grade Highway

In order to study the short-time traffic flow prediction on high-grade highway, the article proposed a model based on wavelet analysis and RBF neural network. Aiming to the traffic flow’s characteristic of highway, the study focus on three facet: network topology, the difference of continuous flow and discontinuous flow , the flow of lanes’ uplink and downlink are not equal. Thus the article use the wavelet analysis to do data preprocessing, then structure the model of short-term traffic flow prediction based on RBF neural network. The experiment result shows that the new hybrid model adapt to high-grade highway, and model considering traffic flow characteristic is better than the model which is not. Meanwhile the model has the higher precision of prediction.

Short-Time Traffic Flow Prediction Based on Chaos Time Series Theory

Traffic flow prediction has become a kernel study in intelligent transportation system. A prediction model of short-time traffic flow is presented based on chaotic time series analysis method. After the phase space reconstruction using traffic flow data, a two-step optimized selection method is proposed, which considers Euclidean distance and equal coefficient between neighboring point and predicted point. Then the prediction model is educed with the local polynomial method to approximate the neighboring points. The model proposed in this paper is applied to predict the real traffic flow in Dongjiang road, Dongguan city in Guangdong province, China. Comparing the predicted traffic flow value with the flow measured in reality, the results show that the maximal relative error is 0.445%, whereas, the minimal one is 0.038%. Moreover, the single-step forward prediction only requires 38.52 seconds. As a result, it is proved that the method can significantly improve the prediction accuracy and meet the requirement of the real-time prediction.