Accuracy Of Traffic Flow Prediction Research Articles

Clustering Urban Roads Using Local Binary Patterns to Enhance the Accuracy of Traffic Flow Prediction

Many studies employ a dynamic statistical approach to model the relationship between roads for predicting traffic flow. Statistical relationships among roads pertain to the associations between road segments in nearby areas. Roads with similar traffic patterns usually define the relationship. Previous studies have shown that adjacent roads demonstrated a similar traffic pattern on the same day and time interval. Studying similar patterns between roads in surrounding areas provides information about the traffic state among roads in a cluster. Furthermore, the results of this finding may be considered to increase the performance of prediction of traffic conditions. In general, road segment roads are correlated with other road segments. They are connected upstream, downstream, or both; they are connected upstream, downstream, or both. To address this, we propose a Local Binary Pattern (LBP) to explore road connections by clustering congestion features between the target roads and their neighboring roads. The outcomes show that the roads with high connectivity obtained from the clustering process were utilized to predict traffic conditions using the SVM approach. The David-Bouldin Index of LBP with k-means shows the lowest compared with PCA k-means and k-means only. By evaluating the clustering result using the Davies-Bouldin Index and assessing the prediction results with SVM, we discovered that incorporating LBP with K-Means improved outcomes in identifying highly connected roads using K-Means.

Exploring the Relationship between Data Sample Size and Traffic Flow Prediction Accuracy

Efficiently extracting and analyzing large urban traffic data, accurately predicting traffic conditions, and improving urban traffic management require careful selection of an appropriate data sample size. The suitable size of data sample assumes paramount importance in fostering sustainable transportation development. This paper investigates the relationship between traffic flow prediction performance and data sample size, considering data sample missing rates, duration, and road segment coverage. Real traffic flow data from 13 road sections in Changsha, China, are analyzed using the Decision Tree, Support Vector Machine, Gaussian Process Regression and Artificial Neural Network models. Some key findings include: Lower data sample loss rates improve prediction accuracy by capturing traffic flow patterns effectively, while higher loss rates decrease accuracy; an optimal data sample duration of around 7 days balances prediction accuracy and data stability, with longer durations providing more historical data but risking complexity; Broader road segment coverage gives a more comprehensive traffic flow information, but excessive coverage introduces noise and impacts the improvement of prediction accuracy. The results highlight the significant impact of data sample size on prediction performance. Enhancing reliability can be achieved by reducing data loss, selecting suitable durations, and considering appropriate road segment coverage, supporting improved traffic management and route planning.

Internet of things and optimized knn based intelligent transportation system for traffic flow prediction in smart cities

The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595.

A traffic-weather generative adversarial network for traffic flow prediction for road networks under bad weather

Traffic flow prediction is pivotal in providing reliable information for intelligent traffic systems. Unexpected events, such as bad weather, unavoidably impact the precision of traffic flow prediction. Therefore, to achieve accurate traffic flow prediction results in road networks under bad weather, a novel Traffic-Weather Generative Adversarial Network (TWeather-GAN model) is developed. This model comprises a Generator and a Discriminator. The Generator incorporates both the traffic and weather modules to extract the spatiotemporal patterns hidden in traffic flow and weather data. In the traffic and weather modules, the gated convolutional layer, Encoder-Decoder architecture, and attention mechanism are established. In the Discriminator, the gated convolutional layer and bidirectional long short-term memory neural network are introduced. Traffic flow data under fog, strong wind, and heavy rain are selected to test the seven baseline models and the proposed model, and the ablation experiments are conducted to analyze the mechanism of the proposed model. The experiments demonstrate that the TWeather-GAN model outperforms the baseline models under bad weather, and makes the prediction error have an average reduction of 0.20%–34.81%, 3.21%–35.22%, and 9.46%–39.10%, respectively, under one-step prediction, three-step prediction, and six-step prediction. Furthermore, establishing the gated convolutional layer and the weather module enhances the accuracy of traffic flow prediction under bad weather. Results show that traffic flow fluctuations and distributions differ under fog and strong wind, and heavy rain affects the trend of traffic flow over one day.

Adaptive Graph Convolutional Recurrent Network with Transformer and Whale Optimization Algorithm for Traffic Flow Prediction

Accurate traffic flow prediction plays a crucial role in the development of intelligent traffic management. Despite numerous investigations into spatio-temporal methods, achieving high accuracy in traffic flow prediction remains challenging. This challenge arises from the complex dynamic spatio-temporal correlations within the traffic road network and the limitations imposed by the selection of hyperparameters based on experiments and manual experience, which can affect the performance of the network architecture. This paper introduces a novel transformer-based adaptive graph convolutional recurrent network. The proposed network automatically infers the interdependencies among different traffic sequences and incorporates the capability to capture global spatio-temporal correlations. This enables the dynamic capture of long-range temporal correlations. Furthermore, the whale optimization algorithm is employed to efficiently design an optimal network structure that aligns with the requirements of the traffic domain and maximizes the utilization of limited computational resources. This design approach significantly enhances the model’s performance and improves the accuracy of traffic flow prediction. The experimental results on four real datasets demonstrate the efficacy of our approach. In PEMS03, it improves MAE by 2.6% and RMSE by 1.4%. In PEMS04, improvements are 1.6% in MAE and 1.4% in RMSE, with a similar MAPE score to the best baseline. For PEMS07, our approach shows a 4.1% improvement in MAE and 2.2% in RMSE. On PEMS08, it surpasses the current best baseline with a 3.4% improvement in MAE and 1.6% in RMSE. These results confirm the good performance of our model in traffic flow prediction across multiple datasets.

IMPROVING TRAFFIC DENSITY PREDICTION USING LSTM WITH PARAMETRIC ReLU (PReLU) ACTIVATION

In the presence of complex traffic flow patterns, this research responds to the challenge by proposing the application of the Long Short-Term Memory (LSTM) model and comparing four different activation functions, namely tanh, ReLU, sigmoid, and PReLU. This research aims to improve the accuracy of traffic flow prediction through LSTM model by finding the best activation function among tanh, relu, sigmoid, and PReLU. The method used starts from the collection of traffic flow datasets covering the period 2015-2017 used to train and evaluate the LSTM model with the four activation functions. Tests were conducted by observing the Train Mean Squared Error (MSE) and Validation MSE. The experimental results show that PReLU provides the best results with a Train MSE of 0.000505 and Validation MSE of 0.000755. Although tanh, ReLU, and sigmoid provided competitive results, PReLU stood out as the optimal choice to improve the adaptability of the model to complex traffic flow patterns.

Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis

The maritime industry is one of the most crucial sectors in the global economy, facilitating the transportation of goods and commodities across vast distances. However, maritime network congestion has become an increasingly critical challenge that significantly affects shipping efficiency and the overall operational performance of the industry. This study proposes an innovative congestion prediction approach using dynamic big data analysis of vessel trajectories and multiscale feature analysis. First, the dynamic analysis of vessel trajectories aims to extract valuable information from ships’ data as they navigate the oceans, enabling proactive traffic management and optimized routing. Second, the multiscale feature analysis provides a comprehensive understanding of maritime network congestion by examining it from different perspectives and scales, leading to more accurate predictions and effective congestion management strategies. Furthermore, this study introduces an enhanced Faster R‐CNN vessel detection model for real‐time tracking, integrating convolutional and SKNet networks. To improve short‐term traffic flow prediction accuracy, this study employs multiscale feature analysis through wavelet transformation. The foundational traffic data undergo wavelet decomposition for a detailed representation across frequencies. Gated recurrent unit (GRU) neural network and autoregressive moving average (ARMA) models are utilized to predict trend and noise components, respectively. Fusion of predictions demonstrates superior accuracy and is validated against real data. Empirical results showcase minimal errors and heightened prediction accuracy compared to actual data.

PLU-MCN: Perturbation learning enhanced U-shaped multi-graph convolutional network for traffic flow prediction

Traffic flow prediction is a challenging task in intelligent transportation systems. To improve the accuracy of traffic flow prediction, graph convolutional neural networks and traffic networks are usually combined to reveal dynamic spatial-temporal correlations. However, the existing graph convolution methods need to repeatedly preprocess data of the same type due to network fluctuations, hardware failures, and other troublesome factors. To address this challenge, a novel Perturbation Learning enhanced U-shaped Multi-graph Convolutional Network (PLU-MCN) is proposed. Specifically, PLU-MCN consists of a Perturbation Learning (PL) module and a U-shaped Multi-graph Convolutional Network (U-MCN) based on encoder-decoder architecture. The PL module is designed based on residual convolution and residual transpose convolution to learn perturbation-resistant features from historical time series in the presence of perturbations. The U-MCN consists of multi-graph convolutional modules based on U-Net to extract dynamic spatial-temporal features from the internal and external information of the perturbation-resistant features and output the final predicted values. Compared with existing state-of-the-art models, the PLU-MCN model demonstrated excellently predictive accuracy and reliability in experiments based on five real-world datasets. The key source code and data are available at https://github.com/Bounger2/PLUMCN.

Extracting long‐term spatiotemporal characteristics of traffic flow using attention‐based convolutional transformer

AbstractPredicting traffic flow is vital for optimizing transportation efficiency, reducing fuel consumption, and minimizing commute times. While artificial intelligence tools have been effective in addressing this, there have been some difficulties in processing spatial and temporal data. Current transformer‐based methods, although cutting‐edge for traffic prediction, encounter challenges with handling long sequences and capturing temporal relations effectively. Addressing these, the research introduces a model combining multi‐scale attention modules within transformer layers. This model employs spatio‐temporal transformer blocks, enriched with multi‐scale convolutional attention mechanisms, allowing for a deeper understanding of temporal and spatial traffic patterns. This unique attention mechanism enhances data feature interpretation, leading to heightened prediction precision. The tests on extensive traffic datasets showcase the model's prowess in capturing both local and global traffic features, resulting in superior traffic status predictions. In summary, the innovative model offers an efficacious approach to long‐sequence traffic data learning and temporal relationship extraction, setting a new benchmark in traffic flow prediction accuracy.

Spatio-temporal graph attention networks for traffic prediction

ABSTRACT The constraints of road network topology and dynamically changing traffic states over time make the task of traffic flow prediction extremely challenging. Most existing methods use CNNs or GCNs to capture spatial correlation. However, convolution operator-based methods are far from optimal in their ability to fuse node features and topology to adequately model spatial correlation. In order to model the spatio-temporal features of traffic flow more effectively, this paper proposes a traffic flow prediction model, the Spatio-Temporal Graph Attention Network (STGAN), which is based on graph attention mechanisms and residually connected gated recurrent units. Specifically, a graph attention mechanism and a random wandering mechanism are used to extract spatial features of the traffic network, and gated recurrent units with residual connections are used to extract temporal features. Experimental results on real-world public transportation datasets show that our approach not only yields state-of-the-art performance, but also exhibits competitive computational efficiency and improves the accuracy of traffic flow prediction.

Road traffic flow prediction based on dynamic spatiotemporal graph attention network

To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the macroscopic periodic characteristics of traffic flow, the spatiotemporal features are extracted by constructing spatiotemporal blocks with an adjacent period, daily period, and weekly period respectively. The spatiotemporal block is mainly composed of a two-layer graph attention network and a gated recurrent unit to capture the hidden features of space and time. In space, based on considering adjacent road segments, the Pearson correlation coefficient is used to capture the hidden correlation characteristics between non-adjacent road segments according to a certain time step. In terms of time, due to the random disturbance of traffic flow at the micro level, the attention mechanism is introduced to use the adjacent time as the query matrix to weight the output characteristics of daily cycle and weekly cycle, and the three are connected in series to output the prediction results through the linear layer. Finally, the experimental results on the public data sets show that the proposed model is superior to the six baseline models.

Gated Recurrent Unit Embedded with Dual Spatial Convolution for Long-Term Traffic Flow Prediction

Considering the spatial and temporal correlation of traffic flow data is essential to improve the accuracy of traffic flow prediction. This paper proposes a traffic flow prediction model named Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU). In particular, the GRU is embedded with the DSC unit to enable the model to synchronously capture the spatiotemporal dependence. When considering spatial correlation, current prediction models consider only nearest-neighbor spatial features and ignore or simply overlay global spatial features. The DSC unit models the adjacent spatial dependence by the traditional static graph and the global spatial dependence through a novel dependency graph, which is generated by calculating the correlation between nodes based on the correlation coefficient. More than that, the DSC unit quantifies the different contributions of the adjacent and global spatial correlation with a modified gated mechanism. Experimental results based on two real-world datasets show that the DSC-GRU model can effectively capture the spatiotemporal dependence of traffic data. The prediction precision is better than the baseline and state-of-the-art models.

Traffic flow prediction under multiple adverse weather based on self-attention mechanism and deep learning models

To improve the accuracy of traffic flow prediction under adverse weather, a deep hybrid attention (DHA) model including the traffic and weather blocks is proposed. The traffic block introduces the convolutional neural network (CNN) and the gated recurrent unit (GRU) neural network to capture the spatio-temporal rules of the traffic flow data. To consider the impacts of adverse weather on traffic flow, the weather block is introduced. The weather block utilizes the convolutional long short-term memory (ConvLSTM) neural network to extract the relationship between the weather and traffic flow data. The self-attention mechanism is embedded in the two blocks. Four cases involving rainy, foggy and windy weather are used to verify the DHA model. The experiments reveal that: the DHA model shows the excellent performance under multiple adverse weather; different adverse weather has various impacts on the rules of traffic volume and speed; the prediction accuracy of each model reduces with the increase of the severe degree of each type of adverse weather.

A Survey Paper on Traffic Prediction Using Machine Learning

Abstract: Traffic flow prediction is an essential tool for traffic management, which can assist traffic operators in making informed decisions for traffic control and management. Accurate traffic flow prediction can also help commuters to plan their journeys efficiently and reduce travel time. The use of Support Vector Regression algorithm for traffic flow prediction has shown promising results in various studies, and it can be used to predict traffic flow at different time intervals, such as hourly, daily, weekly, or even yearly. Additionally, the application of machine learning and artificial intelligence techniques can further enhance the accuracy of traffic flow prediction, which can lead to more effective traffic management strategies

Traffic Flow Prediction Based on Multi-Spatiotemporal Attention Gated Graph Convolution Network

Accurate prediction of traffic flow plays an important role in ensuring public traffic safety and solving traffic congestion. Because graph convolutional neural network (GCN) can perform effective feature calculation for unstructured data, doing research based on GCN model has become the main way for traffic flow prediction research. However, most of the existing research methods solving this problem are based on combining the graph convolutional neural network and recurrent neural network for traffic prediction. Such research routines have high computational cost and few attentions on impaction of different time and nodes. In order to improve the accuracy of traffic flow prediction, a gated attention graph convolution model based on multiple spatiotemporal channels was proposed in this paper. This model takes multiple time period data as input and extracts the features of each channel by superimposing multiple gated temporal and spatial attention modules. The final feature vector is obtained by means of weighted linear superposition. Experimental results on two sets of data show that the proposed method has good performance in precision and interpretability.

Short‐term traffic flow prediction approach incorporating vehicle functions from RFID‐ELP data for urban road sections

Short-term traffic flow prediction is particularly important for urban traffic control and congestion management. To improve the accuracy of traffic flow prediction, a new traffic flow prediction approach is proposed from the perspective of vehicle functions by using Radio Frequency Identification (RFID) electronic license plate (ELP) data. First, the improved Wavelet Neural Network (WNN) is introduced by adding three momentum terms to avoid local minimum and overfitting. The Long Short-Term Memory Neural Network (LSTM) is constructed to train the time series with long time lags. Second, a novel short-term traffic flow prediction method is developed by combining the improved WNN and LSTM in an urban road section. The proposed method performs a weighted combination to obtain a new prediction value according to whether the difference between the prediction values of the two methods satisfy the defined threshold, and then selects the one with the smallest error among the three prediction values as the final prediction result. Third, the experiments explore the prediction performance of the weekdays and weekends with 5 min-interval, 15 min-interval and 30 min-interval via using RFID-ELP data from Chongqing, China. Compared with existing prediction methods, the proposed approach can promote prediction performance and meet real-time requirements.

Combined Traffic Flow Prediction Based on Graph Convolution

Traffic flow data has strong temporal and spatial correlation. The traffic flow in the previous moment will affect the traffic flow in the next moment, and the traffic flow in the upstream and downstream will affect each other in space. . In order to alleviate traffic congestion and improve the accuracy of traffic flow prediction, this paper proposes a combined traffic flow prediction model C GCN based on graph convolution. product to extract the temporal features of the traffic flow. The experimental results show that the prediction effect of the C- GCN combination prediction model is better.

A Hybrid Model for Short-Term Traffic Flow Prediction Based on Variational Mode Decomposition, Wavelet Threshold Denoising, and Long Short-Term Memory Neural Network

Traffic flow prediction plays an important role in intelligent transportation system (ITS). However, due to the randomness and complex periodicity of traffic flow data, traditional prediction models often fail to achieve good results. On the other hand, external disturbances or abnormal detectors will cause the collected traffic flow data to contain noise components, resulting in a decrease in prediction accuracy. In order to improve the accuracy of traffic flow prediction, this study proposes a mixed traffic flow prediction model VMD-WD-LSTM using variational mode decomposition (VMD), wavelet threshold denoising (WD), and long short-term memory (LSTM) network. Firstly, we decompose the original traffic flow sequence into K components through VMD and determine the number of components K according to the sample entropy of different K values. Then, each component is denoised by wavelet threshold to obtain the denoised subsequence. Finally, LSTM is used to predict each subsequence, and the predicted values of each subsequence are combined into the final prediction results. In addition, the performance of the proposed model and the latest traffic flow prediction model is compared on the several well-known public datasets. The empirical analysis shows that the proposed model not only has good prediction accuracy but also has superior robustness.

A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction

With the rapid development of social economy, the traffic volume of urban roads has raised significantly, which has led to increasingly serious urban traffic congestion problems, and has caused much inconvenience to people’s travel. By focusing on the complexity and long-term dependence of traffic flow sequences on urban road, this paper considered the traffic flow data and weather conditions of the road section comprehensively, and proposed a short-term traffic flow prediction model based on the attention mechanism and the 1DCNN-LSTM network. The model combined the time expansion of the CNN and the advantages of the long-term memory of the LSTM. First, the model employs 1DCNN network to extract the spatial features in the road traffic flow data. Second, the output spatial features are considered as the input of LSTM neural network to extract the time features in road traffic flow data, and the long-term dependence characteristics of LSTM neural network are adopted to improve the prediction accuracy of traffic flow. Next, the spatio-temporal characteristics of road traffic flow were regarded as the input of the regression prediction layer, and the prediction results corresponding to the current input were calculated. Finally, the attention mechanism was introduced on the LSTM side to give enough attention to the key information, so that the model can focus on learning more important data features, and further improve the prediction performance. The experimental results showed that the prediction effect of the 1DCNN-LSTM-Attention model under the weather factor was better than that without considering the weather factor. At the same time, compared with traditional neural network models, the prediction effect of the proposed model revealed faster convergence speed and higher prediction accuracy. It can be found that for short-term traffic flow prediction on urban roads, the 1DCNN-LSTM network structure considering the attention mechanism provides superior features.

Traffic prediction based on GCN-LSTM model

Traffic flow prediction is an important part of intelligent traffic management system. Because there are many irregular data structures in road traffic, in order to improve the accuracy of traffic flow prediction, this paper proposes a combined traffic flow prediction model based on deep learning graph convolution neural network (GCN), long-term memory network (LSTM) and residual network (RESNET). GCN is used to extract the features of topology structure in traffic data, LSTM is used to extract the features of time structure, combined with ResNet to optimize the overall model, reduce the occurrence of gradient disappearance or explosion in network degradation, and finally achieve the prediction of traffic flow. According g to the experimental results, the combined traffic flow prediction model used in this paper is closer to the actual traffic flow occurrence than the traditional convolutional neural network model (CNN), and the accuracy is improved.