Short-term Traffic Flow Prediction Research Articles

Short-term traffic flow prediction via weight optimization of composite models

ABSTRACT Accurate prediction of peak-hour traffic flow is crucial for congestion management. Neural network models have varying performance in different domains, limiting their effectiveness in traffic data. To address this, we propose the SSA-BiLSTM-BP-Elman ensemble model. It integrates bi-directional long short-term memory (BiLSTM), back propagation (BP) neural network, and Elman network. The model uses the sparrow search algorithm (SSA) to optimize the weight distribution to improve the prediction accuracy. Initially, the BiLSTM, BP, and Elman models are parameter-optimized by the Bayesian approach. SSA then assigns unique weights to each model based on the characteristics of the extracted data features. Results from an application to UK high-speed traffic flow data show that SSA significantly improves the accuracy of model predictions. This integrated model effectively utilizes the strengths of each model by assigning appropriate weighting coefficients by SSA, thus improving the overall prediction performance.

Short-term traffic flow prediction in heterogeneous traffic conditions using Gaussian process regression

Short-term traffic flow prediction in heterogeneous traffic conditions using Gaussian process regression

Short-term traffic flow prediction based on vehicle trip chain features

ABSTRACT Short-term traffic flow prediction can improve the efficiency of transportation operations. Historical data-driven prediction methods have been proved to perform well. However, saturated or oversaturated traffic operations cannot be accurately predicted based only on detector data from a single intersection. This study proposes a short-term traffic prediction method based on vehicle trip chain features. First, the video data is pre-processed and quality assessed. Then, vehicle trip chain features are mined to correlate upstream and downstream intersections.Convolutional neural networks and long-short-term-memory model are built next. The model is launched to train the predictor and output the traffic flow for all turns at each approach to the intersection. After cases we demonstrate that the prediction accuracy of CNNs-LSTM is usually better than other methods, especially during oversaturation. In addition, we demonstrate that vehicle trip chain features can improve prediction accuracy and shorten the time consumed by the model.

A Short-Term Traffic Flow Prediction Method for Airport Group Route Waypoints Based on the Spatiotemporal Features of Traffic Flow

To fully leverage the spatiotemporal dynamic correlations in air traffic flow and enhance the accuracy of traffic flow prediction models, thereby providing a more precise basis for perceiving congestion situations in the air route network, a study was conducted on a traffic flow prediction method based on deep learning considering spatiotemporal factors. A waypoint network topology graph was constructed, and a neural network model called graph convolution and self-attention-based long short-term memory neural network (GC-SALSTM) was proposed. This model utilized waypoint flow and network efficiency loss rate as input features, with graph convolution extracting spatial features from the waypoint network. Additionally, a long short-term memory network based on a self-attention mechanism was used to extract temporal features, achieving accurate prediction of waypoint traffic. An example analysis was performed on a typical busy sector of airports in the Central and Southern China region. The effectiveness of adding the network efficiency loss rate as an input feature to improve the accuracy of critical waypoint traffic prediction was validated. The performance of the proposed model was compared with various typical prediction models. The results indicated that, with the addition of the network efficiency loss rate, the root mean square error (RMSE) for eight waypoints decreased by more than 10%. Compared to the historical average (HA), autoregressive integrated moving average (ARIMA), support vector regression (SVR), long short-term memory (LSTM), and graph convolution network and long short-term memory network (GCN-LSTM) models, the RMSE of the proposed model decreased by 11.78%, 5.55%, 0.29%, 2.53%, and 1.09%, respectively. This suggests that the adopted network efficiency loss rate indicator effectively enhances prediction accuracy, and the constructed model exhibits superior predictive performance in short-term waypoint traffic forecasting compared to other prediction models. It contributes to optimizing flight paths and high-altitude air routes, minimizing flight delays and airborne congestion to the greatest extent, thus enhancing the overall efficiency of the entire aviation system.

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.

Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction

To more accurately predict short-term traffic flow, this study posits a sophisticated integrated prediction model, CNN-BiGRU-AAM, based on the additive attention mechanism of a convolutional bidirectional gated recurrent unit neural network. This model seeks to enhance the precision of traffic flow prediction by integrating both historical and prospective data. Specifically, the model achieves prediction through two steps: encoding and decoding. In the encoding phase, convolutional neural networks are used to extract spatial correlations between weather and traffic flow in the input sequence, while the BiGRU model captures temporal correlations in the time series. In the decoding phase, an additive attention mechanism is introduced to weigh and fuse the encoded features. The experimental results demonstrate that the CNN-BiGRU model, coupled with the additive attention mechanism, is capable of dynamically capturing the temporal patterns of traffic flow, and the introduction of isolation forests can effectively handle data anomalies and missing values, improving prediction accuracy. Compared to benchmark models such as GRU, the CNN-BiGRU-AAM model shows significant improvement on the test set, with a 47.49 reduction in the Root Mean Square Error (RMSE), a 30.72 decrease in the Mean Absolute Error (MAE), and a 5.27% reduction in the Mean Absolute Percentage Error (MAPE). The coefficient of determination (R2) reaches 0.97, indicating the high accuracy of the CNN-BiGRU-AAM model in traffic flow prediction. It provides a good solution for short-term traffic flow with spatio-temporal features, thereby enhancing the efficiency of traffic management and planning and promoting the sustainable development of transportation.

Short-term traffic flow prediction at isolated intersections based on parallel multi-task learning

This paper proposes a novel phase-based short-term traffic flow prediction method based on parallel multi-task learning for isolated intersections. Different from traditional short-term traffic flow prediction methods, we take the traffic flow of each phase as the minimum prediction unit, instead of directly utilising the traffic flow of a single lane with large random fluctuations. Meanwhile, we design a novel encoding and decoding structure whereby external influencing factors have been incorporated both into encoding and decoding operations. Furthermore, a fusion strategy is proposed to predict the traffic flow of each phase by integrating the traffic flows of lanes whose right of way are provided by the phase. In the fusion strategy, we develop a parallel multi-task prediction framework whereby a new loss function is defined to improve the prediction accuracy. Finally, the proposed method is tested with the traffic flow data collected from an intersection of South Changsheng Road located in the city of Jiaxing. The findings illustrate that the proposed method can achieve better prediction results at different sampling time scales, compared to the existing short-term traffic flow prediction methods.

Efficient implementation of a wavelet neural network model for short-term traffic flow prediction: Sensitivity analysis

The concept of a smart city is emerging to address significant challenges arising from rapid urbanization, economic growth, and climate change. Innovative technologies can be used as a means to promote sustainable and inclusive urban development. These technolgies include the deployment of the internet of things (IoT), artificial intelligence (AI), energy management, and smart transportation. In a smart city, intelligent transportation systems (ITS) play a vital role in efficient traffic management. This paper explores the use of hybrid artificial intelligence techniques for predicting short-term traffic flow data from M25 motorways in the UK. Since volume traffic flow data are non-stationary, wavelet transform (WT) as a powerful signal analyzer is applied for signal decomposition for the elimination of redundant data from input matrices. The feature selection method based on Gram-Schmidt (GS) is used for the selection of more valuable features. The elimination of redundant data can speed up the learning process and improve the generalisation capability of the prediction models. After a pre-processing stage, a wavelet neural network (WNN) with a simple structure is applied as a powerful prediction tool. Two separate structures are considered for the prediction of weekday and weekend traffic volume data. The experiments explore that the debauchies-4 (db4) wavelet function with 7 decomposition levels leads to the best detection accuracy. Moreover, the range of forecasting, the type of the day, the level of decomposition, and other factors all have an impact on prediction stability. Compared with existing prediction methods, the proposed approach produces lower values of root mean square error (RMSE) and mean absolute percentage error (MAPE) for all step-horizons analyzed. These findings provide valuable implications and insights into the development of an efficient and reliable road condition monitoring system for delivering secure and sustainable transportation services.

Short-term traffic flow prediction based on SAE and its parallel training

Short-term traffic flow prediction based on SAE and its parallel training

A Deep Ensemble Approach for Long-Term Traffic Flow Prediction

AbstractIn the last 50 years, with the growth of cities and increase in the number of vehicles and mobility, traffic has become troublesome. As a result, traffic flow prediction started to attract attention as an important research area. However, despite the extensive literature, traffic flow prediction still remains as an open research problem, specifically for long-term traffic flow prediction. Compared to the models developed for short-term traffic flow prediction, the number of models developed for long-term traffic flow prediction is very few. Based on this shortcoming, in this study, we focus on long-term traffic flow prediction and propose a novel deep ensemble model (DEM). In order to build this ensemble model, first, we developed a convolutional neural network (CNN), a long short-term memory (LSTM) network and a gated recurrent unit (GRU) network as deep learning models, which formed the base learners. In the next step, we combine the output of these models according to their individual forecasting success. We use another deep learning model to determine the success of the individual models. Our proposed model is a flexible ensemble prediction model that can be updated based on traffic data. To evaluate the performance of the proposed model, we use a publicly available dataset. Experimental results show that the developed DEM model has a mean square error of 0.06 and a mean absolute error of 0.15 for single-step prediction; it shows that achieves a mean square error of 0.25 and a mean absolute error of 0.32 for multi-step prediction. We compared our proposed model with many models in different categories; individual deep learning models (i.e., LSTM, CNN, GRU), selected traditional machine learning models (i.e., linear regression, decision tree regression, k-nearest-neighbors regression) and other ensemble models such as random-forest regression. These results also support the claim that ensemble learning models perform better than individual models.

Improving Short-Term Traffic Flow Prediction using Grey Relational Analysis for Data Filtering and Stacked LSTM Modeling

Traffic flow prediction is one of the critical measures to alleviate traffic congestion. Currently, traffic flow prediction research has made some achievements, but there are still some deficiencies. In order to solve the problems of low prediction accuracy, poor real-time performance, and high data dimensions. This paper proposes a new traffic flow prediction method that employs Grey Relation Analysis (GRA) to detect the correlation between detection points, remove insignificant or uncorrelated traffic flow data points, and hence reduce the data dimensionality of the prediction model. Multiple Long Short-Term Memory (LSTM) models are then stacked to establish the traffic flow prediction model, considering that traffic flow is affected by multi-dimensional spatiotemporal factors, incorporating vehicle speed, occupancy, and traffic volume as inputs. We conducted experiments on real datasets, and the results showed that our GRA-SLSTM model improved prediction accuracy by 3.6% compared to other models, while reducing model prediction time by 27.33%. The proposed model’s generalization ability is validated by predicting other detection points, which provides significant references for traffic flow prediction research and practical applications.

Hybrid short-term traffic flow prediction based on the effect of non-linear sequence noise

<abstract> <p>Short-term traffic flow prediction is crucial for intelligent transport systems and mitigating traffic congestion. Therefore, precise prediction of real-time traffic conditions is becoming more important. Currently, the existing prediction models lack the ability to effectively extract spatio-temporal characteristics and fail to adequately account for the impact of non-linear noise. To address these issues, the study proposes a hybrid short-term traffic flow prediction model based on spatio-temporal characteristics. First, the method decomposes the initial spatio-temporal traffic sequence data into multiple modal components using the complementary ensemble empirical modal decomposition method. Then, spatio-temporal characteristics are extracted from the decomposed spatio-temporal components using a deep residual network. The predicted values of each factor are combined to obtain the final predicted values. To validate the model, traffic flow data that is collected at point 4909A on the M25 motorway in London is used. The results indicate that the proposed model outperforms other models in terms of accuracy metrics such as root mean square error, mean absolute percentage error, mean absolute error, mean squared error, and coefficient of determination. Therefore, the model has high accuracy and practicality and exhibits great potential for short-term traffic flow prediction.</p> </abstract>

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.

Construction of Short-Term Traffic Flow Prediction Model Based on IoT and Deep Learning Algorithms

Construction of Short-Term Traffic Flow Prediction Model Based on IoT and Deep Learning Algorithms

Retracted: A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model

Retracted: A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model

MFOA-Bi-LSTM: An optimized bidirectional long short-term memory model for short-term traffic flow prediction

Within Intelligent Transportation Systems (ITSs), modeling of traffic flow assumes a pivotal role, as it is crucial for alleviating traffic congestion and reducing carbon emissions. Developing a reliable and sustainable model for predicting traffic flow is highly challenging due to the fluctuations and nonlinear characteristics of traffic flow. In addition to being a practical problem, accurately forecasting traffic flow provides substantial challenges to the researchers working in this area. The aim of this study is to develop a complex framework for forecasting short-term traffic flow with the purpose of improving prediction accuracy. In order to attain this goal, a Bidirectional Long Short-Term Memory (Bi-LSTM) model with a Modified Firefly Optimization Algorithm (MFOA) known as MFOA-Bi-LSTM has been proposed. The MFOA optimization technique has been utilized to optimize the hyperparameters of the deep learning models. Moreover, the optimization of the Bi-LSTM network prediction model is achieved through the application of the MFOA technique, which is renowned for its ability to converge quickly, maintain robustness and perform extensive global searches. To better capture complex patterns in the input data and increase prediction accuracy, model’s architecture makes use of the bidirectional capabilities of Bi-LSTM layers. This combination of Bi-LSTM and FOA is proposed for optimizing complex sequence modeling tasks where selecting the right hyperparameters for the model is challenging and it allows for automated hyperparameter tuning and often leads to improved model performance. In terms of performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Correlation Coefficient (r), the proposed model significantly outperforms the other selected models and study findings confirm its appropriateness for the predictive task.

Performance analysis of machine learning methods for short-term traffic prediction

With the rapid development of urbanization and the rapid increase of the number of motor vehicles, the problem of urban traffic congestion has become increasingly prominent. The accurate prediction of short-term traffic flow is considered as a promising solution, which can provide a key decision-making basis for route planning and traffic flow scheduling, so that can greatly alleviate or even prevent congestion. Researchers have used many machine learning methods to predict traffic flow, but few people pay attention to the boundaries of different machine algorithms. In this paper, we use AdaBoost, Random Forest, SVM and BP neural network to predict short-term traffic flow in California, which aims to compare the differences in prediction performance of different algorithms and analyze their potential reasons. The results show that, the integration methods such as AdaBoost and Random Forest are quite appropriate to solve the short-term traffic flow, which can obtain an accuracy more than 95%, while prediction made by SVM is less precise than the two aforementioned methods with a 79% accuracy. And BP neural network may be inappropriate if the parameters remain default. The different results are due to the periodicity of the database. Integration methods can recognize the periodicity while the SVM and BP neural network fail to do it. When employing the SVM and BP neural network, the datasets need to be divided within a period to avoid being disturbed by cyclically. Besides, the precise of BP neural network can be improved when adjusting the parameters to the optimal.

Temporal data-driven short-term traffic prediction: Application and analysis of LSTM model

With the development of the economy, the number of private cars has increased, and traffic congestion is becoming increasingly common. In order to prevent traffic congestion, short-term prediction of traffic flow is urgent. This article is based on the data of a Hangzhou elevated bridge from October 4th to 18th, 2015, with a timestamp of 5 minutes and a total of over 4000 pieces of data, to have the Long Short-Term Memory (LSTM) model to be trained and the following days traffic flow to be predicted. The outcomes show that the model has high predictive capability and can reliably forecast future short-term transportation passenger flow, accurately reflecting the trend of data changes. Compared with the Auto-Regressive Moving Average (ARIMA) method with a Mean-Square Error (MSE) of 36, the LSTM model has a MSE of 22, which indicates a smaller MSE, indicating a more accurate prediction performance of LSTM. The LSTM models prediction is beneficial for alleviating traffic congestion, providing great convenience for peoples daily travel, and greatly reducing their travel time.

Learning traffic as videos: Short-term traffic flow prediction using mixed-pointwise convolution and channel attention mechanism

In the construction of intelligent transportation systems, short-term traffic flow prediction is of great significance for the advancement of traffic network management. But due to the presence of many complex factors in both spatial and temporal domains, it remains a complex and challenging task. Existing literature usually employs the convolutional neural network (CNN)-based methods in capturing spatiotemporal correlations. These CNN-based methods often use a single-channel data structure to represent different periodic patterns, which makes the model susceptible to over-parameterization when capturing periodic dependencies and prone to information loss after convolution. To overcome these limitations, this paper presents a hybrid deep learning method for short-term traffic flow prediction. In this method, a video-shaped multi-channel data structure is designed to represent different periodic patterns more efficiently. Next, a new mixed-pointwise convolution is introduced for capturing periodic dependencies without the negative impacts mentioned above. Lastly, an improved channel attention mechanism is proposed to learn channel inter-dependencies with controllable parameter usage. The proposed method is lightweight, yet highly effective. Compared to the state-of-the-art baseline method, it reduces the root mean squared error by up to 6.7% on the PeMSD4 dataset and 13.3% on the PeMSD7 dataset, while also achieving substantial improvement in two additional metrics, exhibiting strong robustness and great scalability across various settings.

Vehicle Trajectory Data Mining for Artificial Intelligence and Real-Time Traffic Information Extraction

It aims to improve the efficiency of information collection and extraction in the current intelligent transportation system, and accurately mine the vehicle trajectory data By using Artificial Intelligence (AI) and Deep Learning methods, the trajectory data generated during vehicle driving are deeply mined and analyzed, and the characteristics of driving behavior of vehicle drivers are modeled and analyzed in detail. Then, a method of mining driving behavior characteristics based on Convolutional Neural Network (CNN) and vehicle trajectory is proposed. Based on the mathematical principle of wavelet packet and Least Square Support Vector Machine (LSSVM), a combined model of trajectory mining is constructed and applied to the short-term prediction of traffic flow. The traffic flow of Binjiang Road and Renmin Road in Guangzhou, Guangdong Province from August 19 to August 21, 2021 is predicted to verify the accuracy of the trajectory mining combined model. The results show that the combination model of data mining has good fitting effect, and the average accuracy is above 0.8. Besides, the effectiveness of the Deep Learning model in driver behavior classification is verified. The accuracy of the classification model is 75.2% for trajectory, and that is 76.8% for driver behavior characteristics. It is of great significance to effectively utilize the knowledge data in Intelligent Transportation System (ITS) and extract valuable information from it, which has certain reference value for the subsequent refined prediction of vehicle behavior.